Faculty/ Krishnan Sundara Rajan
Krishnan Sundara Rajan
Professor and Registrar
mail_outline
rajan[at]iiit.ac.in
language
About
A multi-disciplinary researcher who works on Spatial Informatics including the areas of Geospatial Technology and Remote Sensing and its applications across one or more domains of Sustainability, Agriculture, Water, and Urban Sciences. Current interests are in Spatial Data Science and Geo-AI including Spatio-Temporal data mining, OBIA, Spatial Modelling of human-land-water interactions and Remote-Sensing of the Environment.
Also an active proponent of Open Source through OSGeo-India, FSMI and others.
Areas of Expertise
Spatial data science
GeoAI
Geospatial Technologies
Remote Sensing
Modelling
Selected Publications
Contribution of land use/land cover changes to CO2 emissions across Köppen classified climatic zones in South Asia (India)
Mahesh Pathakoti,Srinivasa Rao Goru,Swastika Mandal,Naga Anjani Remala,Srinivasa Rao Sitiraju,Prakash Chauhan,Krishnan Sundara Rajan,Kanchana Lakshmi Asuri,Mahalakshmi Venkata Dangeti,Santhoshi Thadishetty,Sujatha Peethani,Ibrahim Shaik,Taran Mamidala,Raja Pushpanathan
International Journal of Remote Sensing, IJRS, 2025
@inproceedings{bib_Cont_2025, AUTHOR = {Mahesh Pathakoti, Srinivasa Rao Goru, Swastika Mandal, Naga Anjani Remala, Srinivasa Rao Sitiraju, Prakash Chauhan, Krishnan Sundara Rajan, Kanchana Lakshmi Asuri, Mahalakshmi Venkata Dangeti, Santhoshi Thadishetty, Sujatha Peethani, Ibrahim Shaik, Taran Mamidala, Raja Pushpanathan}, TITLE = {Contribution of land use/land cover changes to
CO2 emissions across Köppen classified climatic
zones in South Asia (India)}, BOOKTITLE = {International Journal of Remote Sensing}. YEAR = {2025}}
The land use, land-use changes, and forestry (LULUCF) sector plays
a significant role in increasing atmospheric carbon dioxide (CO2) in the
current climate change scenario. The present study assessed the longterm CO2 emissions within the LULUCF sectors across India’s climatic
zones as described by Köppen. The continuous combustion of fossil
fuels has led to a rise in CO2 levels by 53 parts per million (ppm),
increasing from 372 to 425 ppm in the Indian region between 2002 to
2022. The investigation reveals a consistent upward trend in CO2 levels
across Köppen climatic zones. The semi-arid (steppe) climate zone
shows an increasing trend of 2.18 ppm year−1, whereas Tundra exhibits
a trend of 2.32 ppm year−1. The rate of change of atmospheric CO2 for
India is 2.20 ppm year−1. A regional correlation exists between CO2
concentration and elevated anthropogenic emissions activities in the
Indo-Gangetic Plains (IGP). Settlements growth has occurred across
various climatic zones, with the Subtropical Highland zone showing
the most significant rise at 54.84%. The findings underscore the urgent
requirement for sustainable land use practices. This study utilized
multivariate linear regression analysis to investigate different CO2
sources across LULUCF classes, using these as independent variables
within Köppen climatic zones. The evaluation focused on identifying
the classes with the highest and lowest contributions to CO2 emissions
throughout the study period. Further training was carried out on
different models for India as a whole and by climatic zone, revealing
that the settlement class is a significant source of CO2 emission. In
contrast, the forest class acts as a substantial sink for CO2.
AUTOMATED ROAD NUMBERING FOR GEOSPATIAL MAPS USING SEARCH ALGORITHMS
Srivathsa Gottipati,Shreya,Jai Bhatnagar,Praveen Paruchuri,Krishnan Sundara Rajan
International Geoscience and Remote Sensing Symposium, IGARSS, 2025
@inproceedings{bib_AUTO_2025, AUTHOR = {Srivathsa Gottipati, Shreya, Jai Bhatnagar, Praveen Paruchuri, Krishnan Sundara Rajan}, TITLE = {AUTOMATED ROAD NUMBERING FOR
GEOSPATIAL MAPS USING SEARCH
ALGORITHMS}, BOOKTITLE = {International Geoscience and Remote Sensing Symposium}. YEAR = {2025}}
A significant task that municipalities or equivalent government organizations across the world perform is street addressing including naming of roads. Due to unstructured ways in which towns and cities grow, this process needs significant thought and effort which in reality results in major delays in terms of action taken on these issues. Meanwhile, the local populace which needs some naming convention for practical needs may take the initiative to arrive at reasonable names for the roads within their vicinity based on their local knowledge. This can result in significant sections of the road network without an official name or named after a landmark or a famous personality in many places across the world. The past 2-3 decades have seen an increasing digitisation of government activities. However, a lot of the street addressing and road naming systems and conventions were derived before the digitisation era, which in most cases means that computational parsability of the addresses or road names that would be important for digital processing would not be accounted for. There has been a significant increase in importance and usage of location-based geocoding services like what3words, Zippr and plus codes in recent times. However, these services provide alphanumeric code-based addressing which is not intuitive for human understanding, lacks directionality, does not account for existing address information nor the geometric relations of road topology. In this paper, we propose usage of (modified versions of) well known AI search algorithms for automated road numbering and evaluate them based on spatial proximity and connectivity on roadmaps of three different cities.
Learning Based Infinite Terrain Generation with Level of Detailing
Aryamaan Jain,Avinash Sharma,Krishnan Sundara Rajan
International conference on 3D Vision, 3DV, 2024
@inproceedings{bib_Lear_2024, AUTHOR = {Aryamaan Jain, Avinash Sharma, Krishnan Sundara Rajan}, TITLE = {Learning Based Infinite Terrain Generation with Level of Detailing}, BOOKTITLE = {International conference on 3D Vision}. YEAR = {2024}}
Infinite terrain generation is an important use case for
computer graphics, games and simulations. However, current techniques are often procedural which reduces their realism. We introduce a learning-based generative framework
for infinite terrain generation along with a novel learningbased approach for level-of-detailing of terrains. Our
framework seamlessly integrates with quad-tree-based terrain rendering algorithms. Our approach leverages image
completion techniques for infinite generation and progressive super-resolution for terrain enhancement. Notably, we
propose a novel quad-tree-based training method for terrain enhancement which enables seamless integration with
quad-tree-based rendering algorithms while minimizing the
errors along the edges of the enhanced terrain. Comparative evaluations against existing techniques demonstrate
our framework’s ability to generate highly realistic terrain
with effective level-of-detailing.
Emissions of Methane from Coal, Thermal power plants and Wetlands and its implications on Atmospheric Methane across the South Asian Region
Mahalakshmi D.Venkata,Prakash Chauhan,Mahesh Pathakoti,Lakshmi Kanchan,Sujatha Peethani,Ibrahim Shaik,Krishnan Sundara Rajan,Vijay Kumar Sagar,Pushapanathan Raja,Yogesh K.Tiwari
EGUsphere, EGUS, 2024
@inproceedings{bib_Emis_2024, AUTHOR = {Mahalakshmi D.Venkata, Prakash Chauhan, Mahesh Pathakoti, Lakshmi Kanchan, Sujatha Peethani, Ibrahim Shaik, Krishnan Sundara Rajan, Vijay Kumar Sagar, Pushapanathan Raja, Yogesh K.Tiwari}, TITLE = {Emissions of Methane from Coal, Thermal power plants and Wetlands and its
implications on Atmospheric Methane across the South Asian Region }, BOOKTITLE = {EGUsphere}. YEAR = {2024}}
Atmospheric methane (CH4) is a potent climate change agent responsible for a fraction of global
20 warming. The present study investigated the spatial and temporal variability of atmospheric
column-averaged (X) CH4 (XCH4) concentrations using Greenhouse gases Observing SATellite
(GOSAT) and TROPOspheric Monitoring Instrument onboard the Sentienl-5 Precursor
(S5P/TROPOMI) data from 2009 to 2022 over the South Asia region. During the study period, the
long-term trends in XCH4 increased from 1700 ppb to 1950 ppb with an annual growth rate of 8.76
ppb year-1 25 . Among all natural and anthropogenic sources of CH4, the rate of increase in XCH4 was
higher over the Mundra thermal power station and Mundra ultra mega power plant
at about 9.62 ppb year-1
, followed by the coal site at about 8.76 ppb year-1
(Korba). With a growth
rate of 8.61 ppb year-1
, the Sundarbans natural wetland competes with coal sites, producing over
30 MT, indicating an equivalent anthropogenic source. For the 15 Indian Agroclimatic zones,
30 significant high emissions of CH4 were observed over the Middle Gangetic Plains (MGP), Trans
Gangetic Plains (TGP), Upper Gangetic Plains (UGP), East Coast Plains & Hills (ECPH), Lower
Gangetic Plains (LGP) and East Gangetic Plains (EGP). Further, the bottom-up anthropogenic CH4
emissions data are mapped against the XCH4 concentrations and found high correlation in the Indo
Gangetic Plains (IGP) region, indicating the hotspots of anthropogenic CH4. The present study
35 highlighted the impact of natural and anthropogenic sources of XCH4 and quantified the spatiotemporal changes in XCH4 at each study site over the Indian region
Neighbouring effect of land use changes and fire emissions on atmospheric CO2 and CH4 over suburban region of India (Shadnagar)
Krishnan Sundara Rajan
Science of the Total Environment, STE, 2024
@inproceedings{bib_Neig_2024, AUTHOR = {Krishnan Sundara Rajan}, TITLE = {Neighbouring effect of land use changes and fire emissions on atmospheric CO2 and CH4 over suburban region of India (Shadnagar)}, BOOKTITLE = {Science of the Total Environment}. YEAR = {2024}}
The present study investigated the effects of land use/land cover (LU/LC) changes on atmospheric carbon dioxide (CO2) and methane (CH4) concentrations over the sub-urban region of India (Shadnagar) using continuous decadal CO2 and CH4 in-situ data measured by the greenhouse gas analyser (GGA). Data was collected from 2013 to 2022 at a 1 Hz frequency. Analysis of the current study indicates that during pre-monsoon, the seasonal maximum of CO2 was 409.91 ± 9.26 ppm (μ ± 1σ), while the minimum during monsoon was about 401.64 ± 7.13 ppm. Post-monsoon has a high seasonal mean CH4 concentration of 2.08 ± 0.06 ppm, while monsoon has a low seasonal mean CH4 concentration of 1.88 ± 0.03 ppm. The primary classes, such as forest, crop, and built-up, were considered to estimate the effect of LU/LC changes on atmospheric CO2 and CH4 concentrations. Between 2005 and 2021, the study's results show that the built-up area at radii of 10 km, 20 km, and 50 km increased by 0.17 %, 0.10 %, and 0.4 %, respectively. While other LU/LC categories declined by 30 %, agriculture areas increased by 30 % on average. As a result, the CO2 and CH4 concentrations at the study site are increased by 6 % (26 ppm) and 6.5 % (140 ppb), respectively. The present study utilised the fire-based carbon emissions data from the Global Fire Emissions Database (GFED) to understand the impact on atmospheric CO2 and CH4. Analysis of the present work investigated the influence of transported airmass on CO2 and CH4 during the pre-monsoon and post-monsoon seasons using the HYSPLIT trajectories and found emissions were from the northwest, southeast, and northeast of the study site. Further, in-situ CO2 and CH4 records are compared against the MIROC4-ACTM simulation, and strong agreement was found with bias of 1.80 ppm and 0.98 ppb for CO2 and CH4, respectively.
Citywide Mobile Air Quality Monitoring using GPS-Enabled Low-Cost IoT Sensors
Shreyash Narendra Gujar,Hitesh Pamireddy,Naga Saiteja Maradani,Sara Spanddhana,Sachin Chaudhari,Krishnan Sundara Rajan
International Conference on Environment Pollution and Prevention, ICEPP, 2024
Abs | | bib Tex
@inproceedings{bib_City_2024, AUTHOR = {Shreyash Narendra Gujar, Hitesh Pamireddy, Naga Saiteja Maradani, Sara Spanddhana, Sachin Chaudhari, Krishnan Sundara Rajan}, TITLE = {Citywide Mobile Air Quality Monitoring using GPS-Enabled Low-Cost IoT Sensors}, BOOKTITLE = {International Conference on Environment Pollution and Prevention}. YEAR = {2024}}
Particulate matter (PM) is a critical air pollutant with severe health implications, yet existing stationary monitoring networks often fail to capture its complete spatial and temporal variability in urban environments. This paper presents a novel approach to city-wide air quality monitoring using low-cost sensors mounted on mobile platforms. To validate this method, a six-month field study was conducted in Hyderabad, India, deploying IoT-enabled devices on four college buses. These mobile devices capture PM concentrations, temperature, relative humidity, GPS coordinates, and vehicle speed twice daily across different parts of the city. The primary objective was to demonstrate the necessity of mobile air pollution monitoring to identify PM variability across diverse urban areas. The data revealed significant variations in PM concentrations across different parts of the city and seasons, highlighting the impact of local activities on air quality. The study examines seasonal trends, area-specific variations, and temporal patterns of PM concentrations, identifying pollution hotspots within the city. It shows how important it is to provide up-to-date, location-specific air quality information to people with pollution-related health issues.
Spatio-Temporal PM Analysis for Event Detection using Low-Cost IoT Sensors
Shreyash Narendra Gujar,Sara Spanddhana,Ayu Parmar,Sachin Chaudhari,Krishnan Sundara Rajan
Future Internet of Things and Cloud, FiCloud, 2024
Abs | | bib Tex
@inproceedings{bib_Spat_2024, AUTHOR = {Shreyash Narendra Gujar, Sara Spanddhana, Ayu Parmar, Sachin Chaudhari, Krishnan Sundara Rajan}, TITLE = {Spatio-Temporal PM Analysis for Event Detection using Low-Cost IoT Sensors}, BOOKTITLE = {Future Internet of Things and Cloud}. YEAR = {2024}}
Local activities have a significant impact on the air quality in every region. A study for two consecutive years (2021-2022) has been conducted in the Gachibowli region of Hyderabad, India, which employs spatially distributed low-cost IoT-based air quality monitors across residential and at traffic junctions and main roads measuring particulate matter (PM), temperature, and humidity. The study shows the observations during three seasons, aiming to establish correlations between the PM spikes and specific events that triggered these spikes. Detailed discussions focus on the variations in PM levels linked to traffic patterns over six months and the Diwali festival in 2021 and 2022. PM concentrations increased 2-3 times the normal range during Diwali and decreased post-Diwali. In the case of traffic-related pollution, 1.5 times higher PM levels were observed during morning and evening peak traffic hours, with significant reductions in afternoons across all months with variations in range depending on the season.
Development of End-to-End Low-Cost IoT System for Densely Deployed PM Monitoring Network: An Indian Case Study
Ayu Parmar,Sara Spanddhana,Ayush Kumar Dwivedi,Chinthalapani Rajashekar Reddy,Ishan Patwardhan,Bijjam Sai Dinesh,Sachin Chaudhari,Krishnan Sundara Rajan,KAVITA VEMURI
frontiers in the internet of things, FIT, 2024
@inproceedings{bib_Deve_2024, AUTHOR = {Ayu Parmar, Sara Spanddhana, Ayush Kumar Dwivedi, Chinthalapani Rajashekar Reddy, Ishan Patwardhan, Bijjam Sai Dinesh, Sachin Chaudhari, Krishnan Sundara Rajan, KAVITA VEMURI}, TITLE = {Development of End-to-End Low-Cost IoT System for Densely Deployed PM Monitoring Network: An Indian Case Study}, BOOKTITLE = {frontiers in the internet of things}. YEAR = {2024}}
Particulate matter (PM) is considered the primary contributor to air pollution and has severe implications for general health. PM concentration has high spatial variability and thus needs to be monitored locally. Traditional PM monitoring setups are bulky, expensive, and cannot be scaled for dense deployments. This paper argues for a densely deployed network of IoT-enabled PM monitoring devices using low-cost sensors, specifically focusing on PM10 and PM2.5, the most health-impacting particulates. In this work, 49 devices were deployed in a region of the Indian metropolitan city of Hyderabad, of which 43 devices were developed as part of this work, and six devices were taken off the shelf. The low-cost sensors were calibrated for seasonal variations using a precise reference sensor and were particularly adjusted to accurately measure PM10 and PM2.5 levels. A thorough analysis of data collected for 7 months has been presented to establish the need for dense deployment of PM monitoring devices. Different analyses such as mean, variance, spatial interpolation, and correlation have been employed to generate interesting insights about temporal and seasonal variations of PM10 and PM2.5. In addition, event-driven spatio-temporal analysis is done for PM2.5 and PM10 values to understand the impact of the bursting of firecrackers on the evening of the Diwali festival. A web-based dashboard is designed for real-time data visualization.
Examining chlorophyll-a concentrations in tropical reservoirs under various land use changes using Sentinel – 2 and Google Earth engine – Bhadra and Tungabhadra, India
Avantika Latwal,Tarun Kondraju,Rehana Shaik,Krishnan Sundara Rajan
Journal of Contaminant Hydrology, JCH, 2024
@inproceedings{bib_Exam_2024, AUTHOR = {Avantika Latwal, Tarun Kondraju, Rehana Shaik, Krishnan Sundara Rajan}, TITLE = {Examining chlorophyll-a concentrations in tropical reservoirs under various land use changes using Sentinel – 2 and Google Earth engine – Bhadra and Tungabhadra, India}, BOOKTITLE = {Journal of Contaminant Hydrology}. YEAR = {2024}}
The understanding of spatio-temporal variation in land use and land cover (LULC) patterns is crucial for managing catchment land use planning, as it directly influences of tropical reservoir water quality and the subsequent
Nutrient Contamination (NC) of unmonitored water bodies. The current research attempts to accurately measure
the influence of LULC and its associated determinants on the quantities of NC loads by using Chl-a as a proxy,
within tropical reservoirs, i.e. Bhadra and Tungabhadra, located in same river catchment. This Chl-a spread
calculated by the Maximum Chlorophyll Index (MCI) derived from Sentinel 2 satellite data products covering the
period from July 2016 to June 2021 were done using Google Earth Engine (GEE) platform. The validation
analysis confirms the robustness of the methodology with a strong correlation between MCI-calculated values
and EOMAP (Earth Observation and Environmental Services Mapping) Chl-a (μg/L) data points for both reservoirs, Bhadra (R2 = 0.64) and Tungabhadra (R2 = 0.68). The findings reveal that, Tungabhadra reservoir
consistently exhibits an excessive spatial distribution of Chl-a spread area (17 km2 to 335 km2
), reflecting
nutrient-rich water inflows, particularly evident during the post-monsoon period. This notable rise could be
linked to harvesting the Kharif crop, resulting in elevated nutrient concentrations. In contrast Bhadra reservoir,
dominated by forested areas, maintains relatively lower Chl-a spread areas (<20 km2
), highlighting its pivotal
role in maintaining water cleanliness and serves as a riparian boundary. In addition, the changes in LULC classes
show a strong relationship with variation in Chl-a during the studied period, for the Bhadra Reservoir R2 = 0.51
(F- statistics = 3.983, p = 0.021), and the Tungabhadra Reservoir R2 = 0.802 (F- statistics = 7.489, p = 0.0143).
This highlights how changes in land use significantly shape contamination dynamics, deepening our understanding of nutrient inputs and contamination drivers in tropical reservoirs.
Temporal variability of atmospheric columnar CO2, CH4, CO and N2O concentrations using ground-based remote sensing FTIR Spectrometer
Mahesh Pathakoti,Mahalakshmi D.V,Kanchana,Krishnan Sundara Rajan,Rajashree Vinod Bothale,Prakash Chauhan,Alok Taori
Advances in Space Research, AiSR, 2024
@inproceedings{bib_Temp_2024, AUTHOR = {Mahesh Pathakoti, Mahalakshmi D.V, Kanchana, Krishnan Sundara Rajan, Rajashree Vinod Bothale, Prakash Chauhan, Alok Taori}, TITLE = {Temporal variability of atmospheric columnar CO2, CH4, CO and N2O concentrations using ground-based remote sensing FTIR Spectrometer}, BOOKTITLE = {Advances in Space Research}. YEAR = {2024}}
Solar absorption spectra collected using a ground-based Fourier Transform Infrared (FTIR) Spectrometer were used to retrieve the column-averaged concentrations (X) of CO2, CH4, CO, and N2O during the years 2016–2019 in the Suburban region of Shadnagar, Telangana, India. For the research period, the diurnal mean amplitudes of XCO2, XCH4, XCO, and XN2O are ∼ 13 ppmv, ∼50 ppbv, ∼13 ppbv, and ∼ 19 ppbv respectively. Strong seasonality at the study site is indicated by the seasonal variations of XCO2 and XCO of 18.3 ppmv and 39.2 ppbv, respectively. Additionally, we compared FTIR data to XCO2 detected by Orbiting Carbon Observatory-2 (OCO-2) and CO columns from Measurements of Pollution in the Troposphere (MOPITT). For the XCO2 and CO columns, the comparison research shows a mean relative bias between FTIR data and satellite data of 0.92 % and 0.71 %, respectively. The FTIR and satellite data show a Pearson correlation coefficient (r) of 0.71 (XCO2, N = 26 co-located) and 0.60 (CO columns, N = 44), respectively. The findings demonstrate a good agreement between our data and satellite data.
Water Quality Estimation Using Remote Sensing Technique: A Case Study of Bhadra Reservoir, Karnataka
Avantika Latwal,Krishnan Sundara Rajan,Rehana Shaik
International Conference on Hydraulics, Water Resources and Coastal Engineering, HYDRO, 2023
@inproceedings{bib_Wate_2023, AUTHOR = {Avantika Latwal, Krishnan Sundara Rajan, Rehana Shaik}, TITLE = {Water Quality Estimation Using Remote Sensing Technique: A Case Study of Bhadra Reservoir, Karnataka}, BOOKTITLE = {International Conference on Hydraulics, Water Resources and Coastal Engineering}. YEAR = {2023}}
In the twenty-first century, water quality monitoring is a growing challenge in inland water bodies. Therefore, monitoring the current state of the water bodies is highly important. The remote sensing data is important for effective monitoring that covers a large area. In recent decades, chlorophyll-a (as a proxy) has been a significant indicator of nutrient contamination and also indicates the qualitative status of the water bodies. Besides, the waterbody's temperature also plays an important role in the biological and chemical processes. Hence, the present study aims to detect the chlorophyll-a spread area and temperature of the Bhadra Reservoir, Tungabhadra River system, India. To explore the quality of the reservoir, Sentinel 2A and Landsat 8 satellite images were used for the years 2017 and 2018. The study results noticed that chlorophyll-a spread percent was higher in summer months compared with the water spread area of the reservoir, i.e., 74.5% in 2017 and 61.4% in 2018 from the total water spread area. The study also revealed that as the temperature rises, the chlorophyll-a spread also increases in the reservoir. It was also observed that the October month has a low chlorophyll-a spread, i.e., 32.04% in 2017 and 24.35% in 2018, because the one side reservoir is covered by forest area. Thus, there is a need to explore further to understand the impact of forest area on the reservoir and the temporal changes of the water quality parameters that decide the water bodies quality.
Detection and mapping of water and chlorophyll-a spread using Sentinel-2 satellite imagery for water quality assessment of inland water bodies
Avantika Latwal,Rehana Shaik,Krishnan Sundara Rajan
Environmental Monitoring and Assessment, EMA, 2023
@inproceedings{bib_Dete_2023, AUTHOR = {Avantika Latwal, Rehana Shaik, Krishnan Sundara Rajan}, TITLE = {Detection and mapping of water and chlorophyll-a spread using Sentinel-2 satellite imagery for water quality assessment of inland water bodies}, BOOKTITLE = {Environmental Monitoring and Assessment}. YEAR = {2023}}
Water quality monitoring of reservoirs is currently a signifcant challenge in the tropical regions of the world due to limited monitoring stations and hydrological data. Remote sensing techniques have proven to be a powerful tool for continuous real-time monitoring and assessment of tropical reservoirs water quality. Although many studies have detected chlorophyll-a (Chl-a) concentrations as a proxy to represent nutrient contamination, using Sentinel 2 for eutrophic or hypereutrophic inland water bodies, mainly reservoirs, minimal eforts have been made for oligotrophic and mesotrophic reservoirs. The present study aimed to develop a modeling framework to map and estimate spatio-temporal variability of Chl-a levels and associated water spread using the Modifed Normalized Diference Water Index (MNDWI) and Maximum Chlorophyll Index (MCI). Moreover, the impact of land use/land cover type of the contributing watershed in the oligo-mesotrophic reservoir, Bhadra (tropical reservoir), for 2018 and 2019 using Sentinel 2 satellite data was analyzed. The results show that the water spread area was higher in the post-monsoon months and lower in the summer months. This was further validated by the correlation with reservoir storage, which showed a strong relationship (R2 = 0.97, 2018; R2 = 0.93, 2019). The estimated Chl-a spread was higher in the winter season, because the reservoir catchment was dominated by deciduous forest, producing a large amount of leaf litter in tropical regions, which leads to an increase in the level of Chl-a. It was found that Chl-a spread in the reservoir, specifcally at the inlet sources and near agricultural land practices (western parts of the Bhadra reservoir). Based on the fndings of this study, the MCI spectral index derived from Sentinel 2 data can be used to accurately map the spread of Chl-a in diverse water bodies, thereby ofering a robust scientifc basis for efective reservoir management.
MODELLING EVACUATION STRATEGIES UNDER DYNAMIC CONDITIONS DUE TO OBSTACLE LOCATIONS BASED ON A SEMANTIC 3D BUILDING MODELS
Krishnan Sundara Rajan,Shreya
International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, IAPRS, 2023
@inproceedings{bib_MODE_2023, AUTHOR = {Krishnan Sundara Rajan, Shreya}, TITLE = {MODELLING EVACUATION STRATEGIES UNDER DYNAMIC CONDITIONS DUE TO OBSTACLE LOCATIONS BASED ON A SEMANTIC 3D BUILDING MODELS}, BOOKTITLE = {International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences}. YEAR = {2023}}
Decadal Analysis of Observed Temperature using P-MiSTIC
Eravelli Ankitha Reddy,Krishnan Sundara Rajan
I-GUIDE Forum, I-GUIDE, 2023
@inproceedings{bib_Deca_2023, AUTHOR = {Eravelli Ankitha Reddy, Krishnan Sundara Rajan}, TITLE = {Decadal Analysis of Observed Temperature using P-MiSTIC}, BOOKTITLE = {I-GUIDE Forum}. YEAR = {2023}}
This study employed the P-MiSTIC method to analyze decadal temperature patterns in contiguous Peninsular India. A gridded temperature dataset from IMD spanning 1990-2020 was utilized, generating three decade-wise datasets. P-MiSTIC identified similar zones in decadal and overall datasets, revealing localized data characteristics. The 30-year trend for diurnal temperature range (DTR) showed a significant increase of 0.002oC/year for all zones, while the Western Himalayas and Karakoram range exhibited higher increasing trends of 0.019oC/year and 0.028oC/year, respectively. The DTR lapse rate over 30 years was quantified as -0.133oC/km, with a sharp decline observed from the first two decades (0.012 & 0.028 oC/km) to the third decade (-0.533 oC/km). Differential elevation-dependent temperature changes were observed, with the Karakoram region displaying the most prominent increase, suggesting the emergence of Elevation Dependent Warming in contiguous Peninsular India.
SEASONAL ANALYSIS OF OBSERVED TEMPERATURE IN CONTIGUOUS PENINSULAR INDIA USING P-MISTIC
Eravelli Ankitha Reddy,Krishnan Sundara Rajan
Asian Conference on Remote Sensing, ACRS, 2023
@inproceedings{bib_SEAS_2023, AUTHOR = {Eravelli Ankitha Reddy, Krishnan Sundara Rajan}, TITLE = {SEASONAL ANALYSIS OF OBSERVED TEMPERATURE IN CONTIGUOUS PENINSULAR INDIA USING P-MISTIC}, BOOKTITLE = {Asian Conference on Remote Sensing}. YEAR = {2023}}
This study analysed temperature data in the contiguous Peninsular India from 1991-2020 using the PMiSTIC method, aiming to identify zones with consistent seasonal temperature patterns. Four seasons were considered: DJF, MAM, JJA, and SON, each revealing distinct temperature zones, especially in the high-elevation Himalayan region, with the Western Himalayan and Karakoram zones consistently identified. These regions exhibit Elevation Dependent Warming (EDW) phenomenon, with diurnal temperature range (DTR) being lowest in JJA and highest in MAM. Over the study period, both regions experienced rising temperatures, particularly in DJF and MAM, with the Western Himalayan region showing a slightly higher increase. JJA and SON also displayed increasing trends. Conversely, DJF saw a decreasing temperature trend. DTR exhibited a seasonal cycle, with lowest values in JJA and a gradual increase in SON. Notably, DTR increased during JJA but decreased in DJF. This analysis sheds light on the spatio-temporal variability of temperature patterns in the region, highlighting the influence of elevation on temperature trends throughout the year and the need to incorporate it in climate models for temperature projections
MODELLING EVACUATION STRATEGIES UNDER DYNAMIC CONDITIONS DUE TO OBSTACLE LOCATIONS BASED ON A SEMANTIC 3D BUILDING MODELS
Shreya,Krishnan Sundara Rajan
INTERNATIONAL SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING, ISPRS, 2023
@inproceedings{bib_MODE_2023, AUTHOR = {Shreya, Krishnan Sundara Rajan}, TITLE = {MODELLING EVACUATION STRATEGIES UNDER DYNAMIC CONDITIONS DUE TO OBSTACLE LOCATIONS BASED ON A SEMANTIC 3D BUILDING MODELS}, BOOKTITLE = {INTERNATIONAL SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING}. YEAR = {2023}}
The evacuation path from inside a building to safe point outside becomes highly unpredictable due to changes in the local geometry or presence of obstacles during a disaster like a fire. During emergencies, Evacuees need appropriate information and hence prediction of an unobstructed path, as it emerges, needs to be computed well. Understanding the exits with its allowable people flow rate; the type - door or alternative exit such as windows, balconies, etc.; and its role as a node in the graph network is important to ensure safe and timely evacuation from a building. The study here evaluates how obstacles present in the evacuation route affect the removal of the last person. These obstacles, such as furniture, decrease the flow rate at which evacuees can escape. A subspace model is proposed for geometric spaces or carpet areas containing obstacles and is used to compute the shortest obstacle-free paths. The occupancy is considered within the subspaces containing obstacles. The proposed method clearly shows that a graphbased path generation using a subspace model improves the computation time, can be dynamically adapted, and can be scalable across geometric spaces. The results clearly show the impact of the obstacles, with a 2x to 6x rise when compared to obstacle-free scenarios.
Modeling & Simulating the Evacuation of a Building Based on Building Floor Plan and Evacuation Strategies.
Shreya,Krishnan Sundara Rajan
International Conference on Geographical Information Systems Theory, Applications and Management, GISTAM, 2023
@inproceedings{bib_Mode_2023, AUTHOR = {Shreya, Krishnan Sundara Rajan}, TITLE = {Modeling & Simulating the Evacuation of a Building Based on Building Floor Plan and Evacuation Strategies.}, BOOKTITLE = {International Conference on Geographical Information Systems Theory, Applications and Management}. YEAR = {2023}}
Accidental fires in public and large buildings not only cause property loss but also can lead to loss of lives. During such emergencies, building evacuation depends on a range of factors including floor plans, exits available, obstructions if any, the occupancy levels of the building, and so on. The study here brings together the spatial, temporal, and path planning possibilities to evaluate fire evacuation strategies for 2D building plans. It provides a geospatial framework to assess the impacts of dynamic changes in the building environment and its impact on evacuation outcomes. In this study, occupancy-based path planning using Pgrouting over an IndoorGML formatted data is combined with modeling their interactions over the path toward the exit to assess the outcomes. This computational approach over the time-dependent path provides interesting insights into determining the number of paths and the need for one or more exits during an emergency. The study shows that integrating the floor plan into path generation and people flow can be a powerful tool for assessing the building environment.
Understanding the Impact of Agricultural Fertilizer Application Over Inflows into Nagarjuna Sagar Reservoir
K TARUN TEJA,Krishnan Sundara Rajan
International Conference on Hydraulics, Water Resources and Coastal Engineering, HYDRO, 2023
Abs | | bib Tex
@inproceedings{bib_Unde_2023, AUTHOR = {K TARUN TEJA, Krishnan Sundara Rajan}, TITLE = {Understanding the Impact of Agricultural Fertilizer Application Over Inflows into Nagarjuna Sagar Reservoir}, BOOKTITLE = {International Conference on Hydraulics, Water Resources and Coastal Engineering}. YEAR = {2023}}
Excessive nutrient loading into the inland water bodies and resulting algal blooms have been a major threat to the water quality of the inland water bodies such as lakes and reservoirs where the water is relatively stagnant for longer duration. To improve the conditions, it is essential to study and understand the role of the contributing factors in increasing the nutrient load coming into the water body from the watershed. In general, the excessive usage of inorganic (NPK) fertilizers are considered to be the spearhead behind the increasing nutrient output from the watershed, but their impact has not been well evaluated due to several constraints. So, this study tries to evaluate the role of the fertilizer application on the nutrient yield from the watershed into an inland water body. Nagarjuna Sagar reservoir and its contributing
Spatiotemporal Cluster Analysis of Gridded Temperature Data - A Comparison Between K-means and MiSTIC
Eravelli Ankitha Reddy,Krishnan Sundara Rajan
International Journal of Scientific Research and Engineering Development, IJSRED, 2023
@inproceedings{bib_Spat_2023, AUTHOR = {Eravelli Ankitha Reddy, Krishnan Sundara Rajan}, TITLE = {Spatiotemporal Cluster Analysis of Gridded Temperature Data - A Comparison Between K-means and MiSTIC}, BOOKTITLE = {International Journal of Scientific Research and Engineering Development}. YEAR = {2023}}
The Earth is a system of numerous interconnected spheres, such as the climate. Climate's global and regional influence requires understanding its evolution in space and time to improve knowledge and forecasts. Analyzing and studying decades of climate data is a data mining challenge. Cluster analysis minimizes data volumes and analyzes behavior by cluster. Understanding invariant behavior is as crucial as understanding variable behavior. Gridded data from two sources: Grided IMD data and CMIP5 HadCM3 decadal experiments, are studied using K-Means and MiSTIC clustering techniques to explore spatiotemporal clustering of maximum and minimum temperatures. The boundaries of k-means clustering correspond with topography. The Indian subcontinent's physiographic, climatic, and topographical characteristics affect MiSTIC's core areas. Both techniques yield overlapping clusters. The datasets' MiSTIC cluster counts varied significantly. The impact of data on this technique is shown in how the datasets group the Himalayas.
Implications of Emission Sources and Biosphere Exchange on Temporal Variations of CO2 and δ13C Using Continuous Atmospheric Measurements at Shadnagar (India)
Mahesh Pathakoti,A. L. Kanchana,D. V. Mahalakshmi,Tania Guha,P. Raja,K. Nalini ,Krishnan Sundara Rajan,M. V. R. Sesha Sai,V. K. Dadhwal
Journal of Geophysical Research: Atmospheres, JGR Atm, 2023
@inproceedings{bib_Impl_2023, AUTHOR = {Mahesh Pathakoti, A. L. Kanchana, D. V. Mahalakshmi, Tania Guha, P. Raja, K. Nalini , Krishnan Sundara Rajan, M. V. R. Sesha Sai, V. K. Dadhwal}, TITLE = {Implications of Emission Sources and Biosphere Exchange on Temporal Variations of CO2 and δ13C Using Continuous Atmospheric Measurements at Shadnagar (India)}, BOOKTITLE = {Journal of Geophysical Research: Atmospheres}. YEAR = {2023}}
Diurnal variation of atmospheric CO2 and its driving factors are studied using high-precision in situ mixing ratio measurements and stable carbon isotopic CO2 (δ 13C-CO2) for the first time over Shadnagar, a suburban site in India, from November 2018 to October 2019. The annual mean mixing ratios of atmospheric CO2 and δ 13 C-CO2 are observed to be 414.76 ± 4.26 ppm and −11.19 ‰ ± 1.63 ‰. Maximum diurnal variability of atmospheric CO 2 was observed in summer monsoon (17.30 ± 9.29 ppm), and the minimum was noticed in winter (7.19 ± 0.11 ppm), indicating strong seasonality in diurnal variability of amplitude. To characterize the atmospheric CO 2 sources, the Miller-Tans model was implemented separately using the CO2 and its δ 13 C-CO2 isotopic data during day and night. However, a strong source signature of δ 13C-CO2 was observed during the night-time of summer monsoon with a slope of −37.42‰ ± 0.73‰, obtained using a reduced major axis regression. The identified source indicates possible emissions from fossil fuel combustion. Further, a Lagrangian back trajectory analysis was performed to study the influence of transport on the observed CO2 mixing ratios. The model trajectory confirms the influence of the Indian summer monsoon on the variations of atmospheric CO 2 mixing ratios. Our study shows that fossil fuel emissions are one of the major contributors of CO 2 in the study location (about ∼2 to 4 ppm) with biogenic fluxes adding a clear seasonality ranging from −8 to 4 ppm.
Three-dimensional view of CO2 variability in the atmosphere over the Indian region
Mahesh Pathakoti,Mahalakshmi D.V,Sreenivas Gaddamidi),S.S. Arun,R.V. Bothale,P. Chauhan,Prakash Chauhan,Krishnan Sundara Rajan,Naveen Chandra
Atmospheric Research, AtmR, 2023
@inproceedings{bib_Thre_2023, AUTHOR = {Mahesh Pathakoti, Mahalakshmi D.V, Sreenivas Gaddamidi), S.S. Arun, R.V. Bothale, P. Chauhan, Prakash Chauhan, Krishnan Sundara Rajan, Naveen Chandra}, TITLE = {Three-dimensional view of CO2 variability in the atmosphere over the Indian region}, BOOKTITLE = {Atmospheric Research}. YEAR = {2023}}
The study reports diurnal and seasonal variations of atmospheric CO2 across multiple locations in India using the dedicated ground-based observations, satellite observations and model simulations. Data from seven sites collected during different time periods are analysed to understand the role of biospheric, fossil fuel fluxes on diurnal and seasonal variations of atmospheric CO2. The study also examines the impact of land use/land cover on the variability of atmospheric CO2 concentration. Results show that CO2 mixing ratios are highest during night and lowest in the afternoon. Ponmudi station shows homogeneous daily CO2 mixing ratios because of an active ecosystem and free of boundary layer influence. A high altitude site Ooty exhibits higher diurnal and seasonal variations, possibly due to entrapped emissions from tourism and settlements, while Nagpur’s (an urban site), CO2 mixing ratios remain moderate throughout the year due to active plantations. The observed CO2 is further compared with the simulations from atmospheric chemistry-transport model. The model is able to cap- ture the observed seasonal cycle all over the study locations.
Does Google Earth CRS induce bias with increasing UTM zone number?
Pragada Archana,Krishnan Sundara Rajan
International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS, 2023
@inproceedings{bib_Does_2023, AUTHOR = {Pragada Archana, Krishnan Sundara Rajan}, TITLE = {Does Google Earth CRS induce bias with increasing UTM zone number?}, BOOKTITLE = {International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing}. YEAR = {2023}}
Google Earth (GE) has been very popular since 2005 amongst remote-sensing science enthusiasts as well as administrators for its ease of use and the large archive of multiresolution temporal data covering the entire globe. With time, Google Earth has evolved to publish layers of imagery with spatial resolution estimated to be better than 30 centimeters(since GE does not publish any information related to accuracy of the imagery). Many scholars have studied the positional accuracy of Google Earth imagery from 2008 till 2018. Improvements in the positional accuracy(absolute as well as relative) were reported by many.In this study,it was attempted to understand if the CRS of Google Earth(GE) is contributing to positional inaccuracies when its imagery is assessed using reliable satellite reference data or GPS surveyed data. Relative assessment of GE location coordinates of ground check points was done using Sentinel-2B satellite imagery. Test imagery over regions of Africa, Canada, India and Australia were used and RMSE values were reported. The radial RMSE for the image check points was computed as 12.5 metres; min-max values in Easting direction were [-23.48 m,8.26 m] while min-max values in Northing direction were [-5.77m, 17.26m]. The paper focuses on planimetric mislocation/accuracy between Google Earth and Sentinel-2B and explores direct or indirect relationship between increasing UTM zone numbers and mislocation values. The paper attempts to emphasize that there is a need for the end-user/consumer to understand that, though Google Earth is undeniably the best go-to-resource with open access and high usability rating , it has its own quality issues and should be used with caution in applications where quality and reliability are at stake. GE is indeed an excellent source of ancillary information but it is expected that the end-user be aware of inherent accuracy issues of Google Earth imagery when consuming in applications ranging from as small as a geo-spatial survey by a school community to larger projects requiring high accuracy and precision like mapping, autonomous navigation, control point surveying or satellite calibration. Index Terms—Google Earth, Sentinel -2B, UTM Zone, Relative Planimetric Accuracy , Reliability , High Precision Applications
Ground-Based Remote Sensing of Total Columnar CO 2 , CH 4 , and CO Using EM27/SUN FTIR Spectrometer at a Suburban Location (Shadnagar) in India and Validation of Sentinel-5P/TROPOMI
Vijay Kumar Sagar,Mahesh Pathakoti,Mahalakshmi D.V,Krishnan Sundara Rajan,Sesha Sai M.V.R,Frank Hase,Darko Dubravica,Mahesh Kumar Sha
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, GRSL, 2022
@inproceedings{bib_Grou_2022, AUTHOR = {Vijay Kumar Sagar, Mahesh Pathakoti, Mahalakshmi D.V, Krishnan Sundara Rajan, Sesha Sai M.V.R, Frank Hase, Darko Dubravica, Mahesh Kumar Sha}, TITLE = {Ground-Based Remote Sensing of Total Columnar CO 2 , CH 4 , and CO Using EM27/SUN FTIR Spectrometer at a Suburban Location (Shadnagar) in India and Validation of Sentinel-5P/TROPOMI}, BOOKTITLE = {IEEE GEOSCIENCE AND REMOTE SENSING LETTERS}. YEAR = {2022}}
Greenhouse gases (GHGs) play an important role in controlling local air pollution as well as climate change. In this study, we retrieved column-averaged dry-air ( X ) mole fractions of carbon dioxide (CO 2 ), methane (CH 4 ), and carbon monoxide (CO) using a ground-based EM27/SUN Fourier transform infrared spectrometer (FTIR). The EM27/SUN spectrometers are widely in use in the COllaborative Carbon Column Observing Network (COCCON). The PROFFAST software provided by COCCON has been used to analyze the measured atmospheric solar absorption spectra. In this letter, the diurnal variation and the time series of daily averaged X CO 2 , X CH 4 , and X CO covering the period from December 2020 to May 2021 are analyzed. The maximum values of X CO 2 , X CH 4 , and X CO are observed to be 420.57 ppm, 1.93 ppm, and 170.40 ppb, respectively. Less diurnal but clear seasonal changes are observed during the study period. X CH 4 and X CO from the Sentinel-5Precursor (S5P)/TROPOspheric Monitoring Instrument (TROPOMI) are compared against the EM27/SUN retrievals. The correlation coefficient for the EM27/SUN retrieved X CH 4 and X CO, with the S5P/TROPOMI products, are 0.75 and 0.94, respectively.
Adaptive & Multi-Resolution Procedural Infinite Terrain Generation with Diffusion Models and Perlin Noise
Aryamaan Jain,Avinash Sharma,Krishnan Sundara Rajan
Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP, 2022
@inproceedings{bib_Adap_2022, AUTHOR = {Aryamaan Jain, Avinash Sharma, Krishnan Sundara Rajan}, TITLE = {Adaptive & Multi-Resolution Procedural Infinite Terrain Generation with Diffusion Models and Perlin Noise}, BOOKTITLE = {Indian Conference on Computer Vision, Graphics and Image Processing}. YEAR = {2022}}
This paper proposes a novel adaptive multi-resolution framework for generating terrains. Our framework combines diffusion-based generative network and novel frequency separated terrain features for terrain patch generation. Additionally, we propose to leverage learnable terrain super-resolution for enhancing generated terrain patch followed by novel kernel-based blending of these patches using Perlin noise to generate infinite terrain with realistic terrain features. We provide a comprehensive quantitative and qualitative evaluation of the proposed framework.
Generation of LOD-2 3D Building Models from High-resolution Stereo Satellite Data Using Remote Sensing and Photogrammetry
Venkumahanti Tejaswini,Pyla Kesava Rao,E. N. Dhanamjaya Rao,Nagaraja Ravoori,S. K. Sinha,Krishnan Sundara Rajan
International Conference on Sustainable Construction Technologies and Advancements in Civil Engineer, ScTACE, 2022
Abs | | bib Tex
@inproceedings{bib_Gene_2022, AUTHOR = {Venkumahanti Tejaswini, Pyla Kesava Rao, E. N. Dhanamjaya Rao, Nagaraja Ravoori, S. K. Sinha, Krishnan Sundara Rajan}, TITLE = {Generation of LOD-2 3D Building Models from High-resolution Stereo Satellite Data Using Remote Sensing and Photogrammetry}, BOOKTITLE = {International Conference on Sustainable Construction Technologies and Advancements in Civil Engineer}. YEAR = {2022}}
Building products designed at each level of detail provide single window solutions for Smart City applications. In view of this, the current study uses the tools and techniques to generate second level of detailed buildings that include building shape and height parameters. The extrusion from 2D has been performed altogether in multiple platforms and 3D data handling software. The basic inputs considered for extracting the polygon shapefiles are obtained from high-resolution stereo pair satellite images acquired using Cartosat-I imagery. Eight ground control points are collected using high precision Differential Global Positioning System having millimeters of accuracy. The image adjustments and Digital Surface Model are generated by using the process and concepts of Photogrammetry such as block creation using Rational Polynomial Coefficient text files, internal geometry set up such as interior and exterior orientation, Tie point generation and control point adjustments, and Automated Terrain Extraction. The thus obtained terrain model is overlaid on ortho photograph and the building heights are extracted and compared by using spatial profile graph as well as stereoscopic visualization methods. The height-derived attribute tables are attached to corresponding building footprints and are visualized in different GIS platforms.
A BIM-Based Approach of Electrical Network Analysis and Applications Using GIS Tools
V Tejaswini,P. Kesava Rao,E. N. Dhanamjaya Rao,Nagaraja Ravoori,S. K. Sinha,Krishnan Sundara Rajan
International Conference on Sustainable Construction Technologies and Advancements in Civil Engineer, ScTACE, 2022
Abs | | bib Tex
@inproceedings{bib_A_BI_2022, AUTHOR = {V Tejaswini, P. Kesava Rao, E. N. Dhanamjaya Rao, Nagaraja Ravoori, S. K. Sinha, Krishnan Sundara Rajan}, TITLE = {A BIM-Based Approach of Electrical Network Analysis and Applications Using GIS Tools}, BOOKTITLE = {International Conference on Sustainable Construction Technologies and Advancements in Civil Engineer}. YEAR = {2022}}
Building Information Modeling is a sophisticated technology possessing intellectual tools that help planners and architects to design sustainable buildings and carryout performance analysis based on various aspects. Apart from the existing building elements, a network connecting each building component is of vital importance in order to generate organized plans. Building Information Modeling when embedded with Geographic Information System delivers versatile applications in the world of both Geo Spatial field of studies as well as Architecture, Engineering and Construction Industry. The current research is one such attempt to understand the power of Geographic Information System tools in building models especially in solving complicated network connections. Designing the electrical network and planning the circuitry information in such a way so as to avoid faulty or poor connections in between the inter circuit elements is successfully achieved with the help of this integration. Initially, building model is generated to the high level of detail and electrical fixtures are organized with in a power distribution system using Revit Architecture. The virtual network wiring made in Building model is physically connected using three dimensional Polyline features in Geographic Information System platform and the created Network of electrical elements is utilized for Geometric Network Analysis.
Seismic Vulnerability Assessment of Steel Fiber Reinforced Shotcrete Lined (SFRS) Tunnel: A Himalayan Case Study
Ambika Srivastav,Neelima stayam,Krishnan Sundara Rajan
Journal of the Geological Society of India, JGSI, 2022
Abs | | bib Tex
@inproceedings{bib_Seis_2022, AUTHOR = {Ambika Srivastav, Neelima Stayam, Krishnan Sundara Rajan}, TITLE = {Seismic Vulnerability Assessment of Steel Fiber Reinforced Shotcrete Lined (SFRS) Tunnel: A Himalayan Case Study}, BOOKTITLE = {Journal of the Geological Society of India}. YEAR = {2022}}
The entire Himalayan arc is predicted to produce a series of significant earthquakes and the subsequent great earthquakes of magnitude 8.0 and higher. A substantial amount of rock tunneling is being carried out in the Himalayan region to meet the growing demand for transportation, power, and other infrastructure projects. Earthquake forces influence the final design of the tunnel, which requires further strengthening of the concrete lining and improvement in tunnel support. Because of increasing importance, it is essential to combine the dynamic forces and displacements produced by seismic ground movements into the design stage for tunnels. In this paper, a numerical analysis has been carried out to assess the seismic performance of a proposed hydropower tunnel of 8.8m diameter (horseshoe-shape) in Uttarakhand, India. The impact of earthquakes on underground structures such as tunnels is often considered to be insignificant. However, the results of this study show how that stress from seismic loads can be damaging to the stability of the tunnel. In this study, a pseudo-static approach was adopted to assess the impact of the earthquake on the tunnel lining for the sections located in different rocks namely slate (Q=3.4), quartzite (Q=6), and dolomitic limestone (Q=5.2), respectively. Pseudo-static analysis findings suggest there is a 35% increase in the lining forces for an impact of earthquake for the tunnel section situated in Slate. Furthermore, displacements and damage-prone areas are determined to assess the damage to the tunnel, which could be helpful for rapid evaluations of potential future damage.
SPATIAL FACTOR ANALYSIS OF MOBILE IOT DATA: A CASE STUDY ON PM ACROSS INDIA
Souradeep Deb,Ayush Kumar Dwivedi,Sachin Chaudhari,Krishnan Sundara Rajan
International Geoscience and Remote Sensing Symposium, IGARSS, 2022
@inproceedings{bib_SPAT_2022, AUTHOR = {Souradeep Deb, Ayush Kumar Dwivedi, Sachin Chaudhari, Krishnan Sundara Rajan}, TITLE = {SPATIAL FACTOR ANALYSIS OF MOBILE IOT DATA: A CASE STUDY ON PM ACROSS INDIA}, BOOKTITLE = {International Geoscience and Remote Sensing Symposium}. YEAR = {2022}}
This paper proposes a novel methodology of analyzing mobile Internet of Things (IoT) data by performing spatial and anthropogenic factor-based thematic interactions with it to retrieve interesting patterns that account for the data variation. In order to test out this methodology, a study is conducted by collecting Particulate Matter (PM) data across India using a mobile IoT node, and look into the neighbouring spatial and anthropogenic factors such as human activities, settlement patterns and vegetation profile corresponding to each geo-location of the PM data. By performing the spatial factor analysis on the mobile IoT data, we evaluated the influence of human activities on PM10 levels, most significantly observed for 0
Development of End-to-End Low-Cost IoT System for Densely Deployed PM Monitoring Network: An Indian Case Study
Ayu Parmar,Spanddhana Sara,Ayush Kumar Dwivedi,Chinthalapani Rajashekar Reddy,Ishan Patwardhan,Sai Dinesh Bijjam,Sachin Chaudhari,Krishnan Sundara Rajan,Kavita Vemuri
Technical Report, arXiv, 2022
@inproceedings{bib_Deve_2022, AUTHOR = {Ayu Parmar, Spanddhana Sara, Ayush Kumar Dwivedi, Chinthalapani Rajashekar Reddy, Ishan Patwardhan, Sai Dinesh Bijjam, Sachin Chaudhari, Krishnan Sundara Rajan, Kavita Vemuri}, TITLE = {Development of End-to-End Low-Cost IoT System for Densely Deployed PM Monitoring Network: An Indian Case Study}, BOOKTITLE = {Technical Report}. YEAR = {2022}}
Particulate matter (PM) is considered the primary contributor to air pollution and has severe implications for general health. PM concentration has high spatial variability and thus needs to be monitored locally. Traditional PM monitoring setups are bulky, expensive and cannot be scaled for dense deployments. This paper argues for a densely deployed network of IoT-enabled PM monitoring devices using low-cost sensors. In this work, 49 devices were deployed in a region of the Indian metropolitan city of Hyderabad out-of this, 43 devices were developed as part of this work and 6 devices were taken off the shelf. The low-cost sensors were calibrated for seasonal variations using a precise reference sensor. A thorough analysis of data collected for seven months has been presented to establish the need for dense deployment of PM monitoring devices. Different analyses such as mean, variance, spatial interpolation and correlation have been employed to generate interesting insights about temporal and seasonal variations of PM. In addition, event-driven spatiotemporal analysis is done for PM values to understand the impact of the bursting of firecrackers on the evening of the Diwali festival. A web-based dashboard is designed for real-time data visualization.
Ground-Based Remote Sensing of Total Columnar CO2, CH4, and CO Using EM27/SUN FTIR Spectrometer at a Suburban Location (Shadnagar) in India and Validation of Sentinel-5P/TROPOMI
Vijay Kumar Sagar,Mahesh Pathakoti,Mahalakshmi D.V,Rajan Krishnan Sundara,Sesha Sai M.V.R,Frank Hase,Darko Dubravica,Mahesh Kumar Sha
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, GRSL, 2022
@inproceedings{bib_Grou_2022, AUTHOR = {Vijay Kumar Sagar, Mahesh Pathakoti, Mahalakshmi D.V, Rajan Krishnan Sundara, Sesha Sai M.V.R, Frank Hase, Darko Dubravica, Mahesh Kumar Sha}, TITLE = {Ground-Based Remote Sensing of Total Columnar CO2, CH4, and CO Using EM27/SUN FTIR Spectrometer at a Suburban Location (Shadnagar) in India and Validation of Sentinel-5P/TROPOMI}, BOOKTITLE = {IEEE GEOSCIENCE AND REMOTE SENSING LETTERS}. YEAR = {2022}}
Greenhouse gases (GHGs) play an important role in controlling local air pollution as well as climate change. In this study, we retrieved column-averaged dry-air (X) mole fractions of carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) using a ground-based EM27/SUN Fourier transform infrared spectrometer (FTIR). The EM27/SUN spectrometers are widely in use in the COllaborative Carbon Column Observing Network (COCCON). The PROFFAST software provided by COCCON has been used to analyze the measured atmospheric solar absorption spectra. In this letter, the diurnal variation and the time series of daily averaged XCO2, XCH4, and XCO covering the period from December 2020 to May 2021 are analyzed. The maximum values of XCO2, XCH4, and XCO are observed to be 420.57 ppm, 1.93 ppm, and 170.40 ppb, respectively. Less diurnal but clear seasonal changes are observed during the study period. XCH4 and XCO from the Sentinel-5Precursor (S5P)/TROPOspheric Monitoring Instrument (TROPOMI) are compared against the EM27/SUN retrievals. The correlation coefficient for the EM27/SUN retrieved XCH4 and XCO, with the S5P/TROPOMI products, are 0.75 and 0.94, respectively
Deep Generative Framework for Interactive 3D Terrain Authoring and Manipulation
Shanthika Shankar Naik,Aryamaan Jain,Avinash Sharma,Rajan Krishnan Sundara
International Geoscience and Remote Sensing Symposium, IGARSS, 2022
@inproceedings{bib_Deep_2022, AUTHOR = {Shanthika Shankar Naik, Aryamaan Jain, Avinash Sharma, Rajan Krishnan Sundara}, TITLE = {Deep Generative Framework for Interactive 3D Terrain Authoring and Manipulation}, BOOKTITLE = {International Geoscience and Remote Sensing Symposium}. YEAR = {2022}}
Automated generation and (user) authoring of the realistic virtual terrain is most sought for by the multimedia applications like VR models and gaming. The most common representation adopted for terrain is Digital Elevation Model (DEM). Existing terrain authoring and modelling techniques have addressed some of these and can be broadly categorised as: procedural modeling, simulation method, and example-based methods. In this paper, we propose a novel realistic terrain authoring framework powered by a combination of VAE and generative conditional GAN model. Our framework is an example-based method that attempt to overcome the limitations of existing methods by learning a latent space from real world terrain dataset. This latent space allows us to generate multiple variants of terrain from a single input as well as interpolate between terrains, while keeping the generated terrains close to real world data distribution. We also developed an interactive tool, that lets the user generate diverse terrains with minimalist inputs. We perform thorough qualitative and quantitative analysis and provide comparison with other SOTA methods. We intend to release our code/tool to academic community.
Geospatial object detection using machine learning-aviation case study
Durga Prasad Dhulipudi,Krishnan Sundara Rajan
Integrated Communications Navigation and Surveillance Conference, ICNS, 2021
@inproceedings{bib_Geos_2021, AUTHOR = {Durga Prasad Dhulipudi, Krishnan Sundara Rajan}, TITLE = {Geospatial object detection using machine learning-aviation case study}, BOOKTITLE = {Integrated Communications Navigation and Surveillance Conference}. YEAR = {2021}}
This paper presents the application of computer vision and machine learning to autonomous approach and landing and taxiing for an air vehicle. Recently, there has been growing interest in developing unmanned aircraft systems (UAS). We present a system and method that uses pattern recognition which aids the landing of a UAS and enhances the human-crewed air vehicle landing. Auto-landing systems based on the Instrument Landing System (ILS) have already proven their importance through decades. The auto-land systems work in conjunction with a radio altimeter, ILS, MLS, or GNSS. Closer to the runway, both under VFR and IFR, pilots are expected to rely on visual references for landing. Modern systems like HUD or CVS allow a trained pilot to manually fly the aircraft using guidance cues from the flight guidance system.Notwithstanding the type of landing and instruments used, typically, Pilots are expected to have the runway threshold markings, aiming point, displacement arrows, and touch down markings/lights insight before Minimum Decision Altitude (MDA). Imaging sensors are the essential standard equipment in crewed and crewless aerial vehicles that are widely used during the landing maneuver. In this method, a dataset of visual objects from satellite images is subjected to pattern recognition training. This trained system learns and then identifies and locates important visual references from imaging sensors and could help in landing and taxiing.
Generation of Spatial Profiles & Mapping of Volcanic Ash Distribution
MALINI K,Krishnan Sundara Rajan
International Symposium on Digital Earth, ISDE, 2021
@inproceedings{bib_Gene_2021, AUTHOR = {MALINI K, Krishnan Sundara Rajan}, TITLE = {Generation of Spatial Profiles & Mapping of Volcanic Ash Distribution}, BOOKTITLE = {International Symposium on Digital Earth}. YEAR = {2021}}
Defining spatial distribution of airborne volcanic ash in the neighbourhood of an erupting volcano is a synoptic scale problem, severely impacting lives and livelihoods. Robust algorithms are needed to model such complex phenomenon from sparse field data. This study investigated optimal modelling of the spatial dispersion of ash using Empirical Bayesian Kriging (EBK): a geostatistical, probabilistic algorithm. Both distance and ash temperature values of samples from the 2010 Icelandic eruption were spatially correlated using semivariograms to generate prediction and error surfaces. Results showed that block averages were 90% accurate as validated against NCEP NWP model data. The work supports the utility of EBK in datasets where spatial autocorrelation is not significant. Furthermore, the results could help generate risk maps to delineate safety zones for aircraft
Automated tree generation using grammar & particle system
Aryamaan Jain,Jyoti Sunkara,Shah Ishaan Nikhil,Avinash Sharma,Rajan Krishnan Sundara
Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP, 2021
@inproceedings{bib_Auto_2021, AUTHOR = {Aryamaan Jain, Jyoti Sunkara, Shah Ishaan Nikhil, Avinash Sharma, Rajan Krishnan Sundara}, TITLE = {Automated tree generation using grammar & particle system}, BOOKTITLE = {Indian Conference on Computer Vision, Graphics and Image Processing}. YEAR = {2021}}
Trees are an integral part of many outdoor scenes and are rendered in a wide variety of computer applications like computer games, movies, simulations, architectural models, AR and VR. This has led to increasing demand for realistic, intuitive, lightweight and easy to produce computer-generated trees. The current approaches at 3D tree generation using a library of trees lack variations in structure and are repetitive. This paper presents an extended grammar-based automated solution for 3D tree generation that can model a wide range of species, both Western and Indian. For the foliage, we adopt a particle system approach that models the leaf, its size, orientation and changes. The proposed solution additionally allows control
IoT Network Based Analysis of Variations in Particulate Matter due to COVID-19 Lockdown
Souradeep Deb,Chinthalapani Rajashekar Reddy,Sachin Chaudhari,Kavita Vemuri,Rajan Krishnan Sundara
International Conference on Electronics, Computing and Communication Technologies, CONECCT, 2021
@inproceedings{bib_IoT__2021, AUTHOR = {Souradeep Deb, Chinthalapani Rajashekar Reddy, Sachin Chaudhari, Kavita Vemuri, Rajan Krishnan Sundara}, TITLE = {IoT Network Based Analysis of Variations in Particulate Matter due to COVID-19 Lockdown}, BOOKTITLE = {International Conference on Electronics, Computing and Communication Technologies}. YEAR = {2021}}
During the COVID-19 pandemic, India’s complete lockdown was implemented from March 24 to May 3 2020, to minimize the effects of community transfer and control the rapidly growing rate of the virus spread. In this paper, we focus on quantifying the change in air pollution due to Hyderabad’s lockdown, the capital of Telangana State. For this, two datasets are employed. The first dataset is from the Central Pollution Control Board (CPCB) stations in the city. In contrast, the second dataset is the dense IoT network of PM monitors deployed in the educational campus of IIITH in Gachibowli, Hyderabad. An analysis is done on the collected data to understand the effect of lockdown on PM values while considering the yearly and seasonal variations. It has been shown that while there has been a significant drop in PM values. However, through correlation analysis between the temperature and the PM values during the regular times, not all PM values decrease because of the lockdown. Index Terms
GENERATING SPATIAL DISTRIBUTION OF VOLCANIC ASH SPREAD
MALINI K,Rajan Krishnan Sundara
International Geoscience and Remote Sensing Symposium, IGARSS, 2021
@inproceedings{bib_GENE_2021, AUTHOR = {MALINI K, Rajan Krishnan Sundara}, TITLE = {GENERATING SPATIAL DISTRIBUTION OF VOLCANIC ASH SPREAD}, BOOKTITLE = {International Geoscience and Remote Sensing Symposium}. YEAR = {2021}}
Generation of spatial profiles of airborne volcanic ash that is spread at synoptic scales is a problem that directly impacts lives and properties. Robust algorithms are needed to model the distribution using sparse data sampled in the neighborhood of an erupting volcano. Existing Numerical Weather Prediction (NWP) algorithms model the dispersion at coarser spatial resolutions. In this study, we evaluate a geospatial interpolation technique called Kriging [1] to generate prediction and error surfaces. Location and temperature values of ash from 2010 Icelandic eruption were spatially autocorrelated using a stochastic kriging method, known as Empirical Bayesian Kriging (EBK) [7]. The EBK estimates were rigorously validated against NWP for regions with varying sample densities. Subsequently, a method to generate an accurate overlay map using EBK estimates to augment NWP outputs is proposed to aid in …
Geospatial Object Detection using Machine Learning-Aviation Case Study
Durga Prasad Dhulipudi,Rajan Krishnan Sundara
Integrated Communications Navigation and Surveillance Conference , ICNS, 2021
@inproceedings{bib_Geos_2021, AUTHOR = {Durga Prasad Dhulipudi, Rajan Krishnan Sundara}, TITLE = {Geospatial Object Detection using Machine Learning-Aviation Case Study }, BOOKTITLE = {Integrated Communications Navigation and Surveillance Conference }. YEAR = {2021}}
This paper presents the application of computer vision and machine learning to autonomous approach and landing and taxiing for an air vehicle. Recently, there has been growing interest in developing unmanned aircraft systems (UAS). We present a system and method that uses pattern recognition which aids the landing of a UAS and enhances the human-crewed air vehicle landing. Auto-landing systems based on the Instrument Landing System (ILS) have already proven their importance through decades. The auto-land systems work in conjunction with a radio altimeter, ILS, MLS, or GNSS. Closer to the runway, both under VFR and IFR, pilots are expected to rely on visual references for landing. Modern systems like HUD or CVS allow a trained pilot to manually fly the aircraft using guidance cues from the flight guidance system. Notwithstanding the type of landing and instruments used, typically, Pilots are expected to have the runway threshold markings, aiming point, displacement arrows, and touch down markings/lights insight before Minimum Decision Altitude (MDA). Imaging sensors are the essential standard equipment in crewed and crewless aerial vehicles that are widely used during the landing maneuver. In this method, a dataset of visual objects from satellite images is subjected to pattern recognition training. This trained system learns and
OPTIMAL ROUTING OF IRRIGATION CANAL PATHS USING A DIGITAL ELEVATION MODEL
PONNATHOTA SAI CHAITANYA REDDY,Rajan Krishnan Sundara
Asian Conference on Remote Sensing, ACRS, 2020
@inproceedings{bib_OPTI_2020, AUTHOR = {PONNATHOTA SAI CHAITANYA REDDY, Rajan Krishnan Sundara}, TITLE = {OPTIMAL ROUTING OF IRRIGATION CANAL PATHS USING A DIGITAL ELEVATION MODEL}, BOOKTITLE = {Asian Conference on Remote Sensing}. YEAR = {2020}}
In developing the infrastructure facilities such as irrigation canals and road networks, topography acts as a significant enabler or constraint. Contour maps and low resolution DEMs have been used by Irrigation engineers and planners to assess the canal routing options, which is time consuming and requires repeated evaluations of the potential paths. So, there is a need to develop robust path planning algorithms, including least cost routing, that takes the topographic and engineering constraints while providing potential canal routing paths. Some recent works have attempted to develop algorithms on synthetic data sets but have not been scaled up on high-resolution data sets, limiting their practical use. This work develops a generic algorithm to determine the least-cost flow path between two geo-locations, given the grid-based Digital Elevation Models (DEMs) and a unit cost of construction per length. From the numerous paths that are possible between the two points in any given topography, a distinct least-cost path is identified. The proposed approach is evaluated by computing canal routing paths over publicly available real-world datasets across two different resolutions of 1Km and 90-meter from different sources for Indian terrains. This is then compared against a global reference real-world dataset, Digital Chart of the World (DCW) at 1Km at a grid cell level. The results across multiple stretches shows an average accuracy of 82.09% as measured based on the path overlap against the DCW dataset, proving that the proposed algorithm can be useful in practice.
DAV - DATA ANALYTICS AND VISUALIZATION SYSTEM FOR ROADS
Bhavana Gannu,Rajan Krishnan Sundara
INTERNATIONAL SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING, ISPRS, 2020
@inproceedings{bib_DAV__2020, AUTHOR = {Bhavana Gannu, Rajan Krishnan Sundara}, TITLE = {DAV - DATA ANALYTICS AND VISUALIZATION SYSTEM FOR ROADS}, BOOKTITLE = {INTERNATIONAL SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING}. YEAR = {2020}}
This paper proposes a system for monitoring of condition and surface of roads in developing countries like India. This system will be used by government agencies to monitor municipal activities like road laying and planning. The system utilizes a database created by geo-citizens or government workers as an input. The heavy machinery in existing systems is not an optimized solution to this problem. Some existing systems use GPS and accelerometer data for determining such artifacts. So, it is evident that there is a need for a system that generates robust, frequent and accountable geo-tagged data. We propose a new collaborative model for such a purpose by fusion of data from multiple sensors hosted on smart-phones of several active geo-citizens. The system focuses mainly on volunteered geographic information, in which users can use their respective smart-phones to collect the data required and upload it for further analysis. The server side of the system infuses this data into a PostGIS database and displays the road condition on a near real-time basis over a WebGIS. The strength of a good visualization in imparting insight to decision-makers is widely recognized. We advance the paper by assessing procured road data and displaying it in an easy to understand format. In addition to visualization, the WebGIS component also provides for timeline analysis of changes in road conditions, which may help in the improved management of road infrastructure.
SHIFTING CULTIVATION PRACTICES IN BARAK VALLEY, INDIA – POLICY SCENARIOS FROM A SPATIALLY EXPLICIT LAND USE MODEL
JYOTI MISRA,Rajan Krishnan Sundara
INTERNATIONAL SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING, ISPRS, 2020
@inproceedings{bib_SHIF_2020, AUTHOR = {JYOTI MISRA, Rajan Krishnan Sundara}, TITLE = {SHIFTING CULTIVATION PRACTICES IN BARAK VALLEY, INDIA – POLICY SCENARIOS FROM A SPATIALLY EXPLICIT LAND USE MODEL}, BOOKTITLE = {INTERNATIONAL SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING}. YEAR = {2020}}
Barak valley is a region in north east part of India where the practice of shifting cultivation is quite prevalent. Population growth coupled with the geographic isolation of the area have led to an increased pressure on land and a consequent decline in forest cover. The decrease of forests observed is spatially distributed and dependent on neighborhood rules. Hence, we look towards modelling the land use change to understand the land use changes and the factors affecting them. In this paper, we modify an agent-based land use model for modelling shifting cultivation to determine how various policy changes at a larger scale might affect the shifting cultivation practice in the region at the micro level. We explore scenarios like drastic population increase and availability of irrigation infrastructure in the area. Through the scenario analysis we explore how policies play a role in agriculture patterns and influence land use patterns.
AFN: Attentional Feedback Network based 3D Terrain Super-Resolution
Kubade Ashish Ashokrao,Diptiben N Patel,Avinash Sharma,Rajan Krishnan Sundara
Asian Conference on Computer Vision, ACCV, 2020
@inproceedings{bib_AFN:_2020, AUTHOR = {Kubade Ashish Ashokrao, Diptiben N Patel, Avinash Sharma, Rajan Krishnan Sundara}, TITLE = {AFN: Attentional Feedback Network based 3D Terrain Super-Resolution}, BOOKTITLE = {Asian Conference on Computer Vision}. YEAR = {2020}}
Terrain, representing features of an earth surface, plays a crucial role in many applications such as simulations, route planning, analysis of surface dynamics, computer graphics-based games, entertainment, films, to name a few. With recent advancements in digital technology, these applications demand the presence of high resolution details in the terrain. In this paper, we propose a novel fully convolutional neural network based super-resolution architecture to increase the resolution of low-resolution Digital Elevation Model (LRDEM) with the help of information extracted from the corresponding aerial image as a complementary modality. We perform the super-resolution of LRDEM using an attention based feedback mechanism named ‘Attentional Feedback Network’ (AFN), which selectively fuses the information from LRDEM and aerial image to enhance and infuse the high-frequency features and to produce the terrain realistically . We compare the proposed architecture with existing state-of-the-art DEM super-resolution methods and show that the proposed architecture outperforms enhancing the resolution of input LRDEM accurately and in a realistic manner.
Feedback Neural Network based Super-resolution of DEM for generating high fidelity features
Kubade Ashish Ashokrao,AVINASH SHARMA,Rajan Krishnan Sundara
International Geoscience and Remote Sensing Symposium, IGARSS, 2020
@inproceedings{bib_Feed_2020, AUTHOR = {Kubade Ashish Ashokrao, AVINASH SHARMA, Rajan Krishnan Sundara}, TITLE = {Feedback Neural Network based Super-resolution of DEM for generating high fidelity features}, BOOKTITLE = {International Geoscience and Remote Sensing Symposium}. YEAR = {2020}}
High resolution Digital Elevation Models (DEMs) are an important requirement for many applications like modelling water flow, landslides, avalanches etc. Yet publicly available DEMs have low resolution for most parts of the world. Despite tremendous success in image super resolution task using deep learning solutions, there are very few works that have used these powerful systems on DEMs to generate HRDEMs. Motivated from feedback neural networks, we propose a novel neural network architecture that learns to add high frequency details iteratively to low resolution DEM, turning it into a high resolution DEM without compromising its fidelity. Our experiments confirm that without any additional modality such as aerial images (RGB), our network DSRFB achieves RMSEs of 0.59 to 1.27 across 4 different datasets.
DAV-DATA ANALYTICS AND VISUALIZATION SYSTEM FOR ROADS.
GANNU BHAVANA,Rajan Krishnan Sundara
ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, ISPRS-APRSS, 2020
@inproceedings{bib_DAV-_2020, AUTHOR = {GANNU BHAVANA, Rajan Krishnan Sundara}, TITLE = {DAV-DATA ANALYTICS AND VISUALIZATION SYSTEM FOR ROADS.}, BOOKTITLE = {ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences}. YEAR = {2020}}
This paper proposes a system for monitoring of condition and surface of roads in developing countries like India. This system will be used by government agencies to monitor municipal activities like road laying and planning. The system utilizes a database created by geo-citizens or government workers as an input. The heavy machinery in existing systems is not an optimized solution to this problem. Some existing systems use GPS and accelerometer data for determining such artifacts. So, it is evident that there is a need for a system that generates robust, frequent and accountable geo-tagged data. We propose a new collaborative model for such a purpose by fusion of data from multiple sensors hosted on smart-phones of several active geo-citizens. The system focuses mainly on volunteered geographic information, in which users can use their respective smart-phones to collect the data required and upload it for …
Improving Spatio-Temporal Understanding of Particulate Matter using Low-Cost IoT Sensors
Chinthalapani Rajashekar Reddy,Mukku Tanmai,Ayush Kumar Dwivedi,AUROPRAVA ROUT,Sachin Chaudhari,Kavita Vemuri,Rajan Krishnan Sundara,Aftab M. Hussain
International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, 2020
@inproceedings{bib_Impr_2020, AUTHOR = {Chinthalapani Rajashekar Reddy, Mukku Tanmai, Ayush Kumar Dwivedi, AUROPRAVA ROUT, Sachin Chaudhari, Kavita Vemuri, Rajan Krishnan Sundara, Aftab M. Hussain}, TITLE = {Improving Spatio-Temporal Understanding of Particulate Matter using Low-Cost IoT Sensors}, BOOKTITLE = {International Symposium on Personal, Indoor and Mobile Radio Communications}. YEAR = {2020}}
Current air pollution monitoring systems are bulky and expensive resulting in a very sparse deployment. In addition, the data from these monitoring stations may not be easily accessible. This paper focuses on studying the dense deployment based air pollution monitoring using IoT enabled low-cost sensor nodes. For this, total nine low-cost IoT nodes monitoring particulate matter (PM), which is one of the most dominant pollutants, are deployed in a small educational campus in Indian city of Hyderabad. Out of these, eight IoT nodes were developed at IIIT-H while one was bought off the shelf. A web based dashboard website is developed to easily monitor the real-time PM values. The data is collected from these nodes for more than five months. Different analyses such as correlation and spatial interpolation are done on the data to understand efficacy of dense deployment in better understanding the spatial variability and time-dependent changes to the local pollution indicators.
Design and Implementation of IoT Solution for Air Pollution Monitoring
Aarathi Ramesh Muppalla,Mahesh Pathakoti,Vinod M Bothale,Biswadip G,Sesha Sai M.V.R,Subramanian V,Rajan Krishnan Sundara
IEEE Recent Advances in Geoscience and Remote Sensing : Technologies, Standards and Applications, TENGARSS, 2019
@inproceedings{bib_Desi_2019, AUTHOR = {Aarathi Ramesh Muppalla, Mahesh Pathakoti, Vinod M Bothale, Biswadip G, Sesha Sai M.V.R, Subramanian V, Rajan Krishnan Sundara}, TITLE = {Design and Implementation of IoT Solution for Air Pollution Monitoring}, BOOKTITLE = {IEEE Recent Advances in Geoscience and Remote Sensing : Technologies, Standards and Applications}. YEAR = {2019}}
—Real time air pollution monitoring is vital in densely populated areas to assess the micro level air pollution. Monitoring the air quality can be useful for providing accurate information to the public and to help policy decision makers to take actions that can improve the quality of air. Monitoring of the pollutants using Internet of Things (IoT) is a cost effective solution. This has the capability to provide high spatial and temporal air pollutants levels depending on the density of the IoT network. In this paper, we present an end to end prototype solution for monitoring of air pollutants. low cost LoraWAN CO2, CO and PM2.5 sensors are used in this prototype application. We also implemented a real time monitoring system using web dashboards with the capability to perform real time analysis using open source software elasticsearch and kibana. A prototype web dashboard showing the real time values of the sensor values along with temperature and humidity values is also developed. Index Terms—Air pollutants, Real time, Visualization, Analysis
Analysis of high variability of inter-state land use land cover change using remote sensing and GIS techniques primarily agriculture in Krishna river basin, India.
YESHU SHARMA,Rajan Krishnan Sundara
Geophysical Research Abstracts, GRA, 2019
@inproceedings{bib_Anal_2019, AUTHOR = {YESHU SHARMA, Rajan Krishnan Sundara}, TITLE = {Analysis of high variability of inter-state land use land cover change using remote sensing and GIS techniques primarily agriculture in Krishna river basin, India.}, BOOKTITLE = {Geophysical Research Abstracts}. YEAR = {2019}}
Analysis of high variability of inter-state land use land cover change using remote sensing and GIS techniques primarily agriculture in Krishna river basin, India.
EXCESSIVE FERTILIZER USAGE DRIVES AGRICULTURE GROWTH BUT DEPLETES WATER QUALITY.
T. T. Kondraju,Rajan Krishnan Sundara
ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, ISPRS-APRSS, 2019
@inproceedings{bib_EXCE_2019, AUTHOR = {T. T. Kondraju, Rajan Krishnan Sundara}, TITLE = {EXCESSIVE FERTILIZER USAGE DRIVES AGRICULTURE GROWTH BUT DEPLETES WATER QUALITY.}, BOOKTITLE = {ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences}. YEAR = {2019}}
By 2050 most parts of India will be water stressed zones as most of the water resources are under heavy stress due to increasing nutrient contamination in their waters. In this scenario, studying the changes occurring in the freshwater nutrient contamination levels over a temporal scale is extremely important. This study focuses on monitoring the changes occurring in the nutrient contamination levels over a decade in a large reservoir known as Nagarjuna Sagar (NS) using remote sensing data. In this study, Landsat (5 & 8) data for the year 2005, 2009, 2015 and Sentinel (2A and 2B) data for the years 2016 and 2018 is used to study nutrient contamination in NS. The spatial spread of chlorophyll-a (chl-a) area is used as a proxy to estimate the extent of nutrient contamination in NS. In this study, only October images of NS are used as they exhibit the maximum spatial spread of Chl-a and hence help assess the contamination levels over the period 2005-2018. The analysis shows that during this period, chl-a spatial spread area has increased from 21 Km2 to 205 Km2, indicating a decrease in water quality in the reservoir. The study shows that this is accompanied by an increase in the agricultural land use area by 1000 Km2 in addition to a steep increase in the use of agricultural inputs, primarily fertilisers like urea, P and K. Thus, while the combined effect of excessive usage of fertilizers with agricultural intensification has increased crop yields, it has also contributed to damaging the freshwater resources.
Development of an Indoor Space Semantic Model and Its Implementation as an IndoorGML Extension
NISHITH MAHESHWARI,SRISHTI SRIVASTAVA,Rajan Krishnan Sundara
ISPRS International Journal of Geo-Information, ISPRS- IJG, 2019
@inproceedings{bib_Deve_2019, AUTHOR = {NISHITH MAHESHWARI, SRISHTI SRIVASTAVA, Rajan Krishnan Sundara}, TITLE = {Development of an Indoor Space Semantic Model and Its Implementation as an IndoorGML Extension}, BOOKTITLE = {ISPRS International Journal of Geo-Information}. YEAR = {2019}}
Geospatial data capture and handling of indoor spaces is increasing over the years and has had a varied history of data sources ranging from architectural and building drawings to indoor data acquisition approaches. While these have been more data format and information driven primarily for the physical representation of spaces, it is important to note that many applications look for the semantic information to be made available. This paper proposes a space classification model leading to an ontology for indoor spaces that accounts for both the semantic and geometric characteristics of the spaces. Further, a Space semantic model is defined, based on this ontology, which can then be used appropriately in multiple applications. To demonstrate the utility of the model, we also present an extension to the IndoorGML data standard with a set of proposed classes that can help capture both the syntactic and semantic components of the model. It is expected that these proposed classes can be appropriately harnessed for use in diverse applications ranging from indoor data visualization to more user customised building evacuation path planning with a semantic overtone. View Full-Text
CoRe: Generating a Computationally Representative Road Skeleton - Integrating AADT with Road Structure
Sankepally Rohith Reddy,Rajan Krishnan Sundara
International Conference on Big Data Analysis and Knowledge Discovery, BDAKD, 2018
@inproceedings{bib_CoRe_2018, AUTHOR = {Sankepally Rohith Reddy, Rajan Krishnan Sundara}, TITLE = {CoRe: Generating a Computationally Representative Road Skeleton - Integrating AADT with Road Structure}, BOOKTITLE = {International Conference on Big Data Analysis and Knowledge Discovery}. YEAR = {2018}}
Road networks are the lifeline of a city and understanding its usage has a number of potential applications from transportation planning and engineering aspects to environmental management. While the full network is important to be analyzed for such applications, prudent planning needs one to identify the significant sections of the road network and prioritize them. Annual Average Daily Traffic (AADT), an estimate of the average daily traffic along a defined road segment, is one such data that helps in such endeavors. But, roads are also about connectivity and accessibility across different regions. Hence, this paper proposes a study that integrates the AADT data with implicit information derived from the road network to generate a computationally representative (CoRe) well connected sub-network, significantly smaller than the original network. While the AADT data analysis looks for road segments with high traffic, this paper proposes and evaluates a graph theory based approach for calculating road priorities purely based on the topological structure of the road network. The work further demonstrates the utility of the CoRe sub-network in terms of both, achieving gains in path computation and capturing the behavioral pattern of travelers. A case study of the Melbourne, Australia supports the feasibility and applicability of this knowledge integration approach.
INTEGRATING MSER INTO A FAST ICA APPROACH FOR IMPROVING BUILDING DETECTION ACCURACY
LIPIKA AGARWAL,Rajan Krishnan Sundara
International Geoscience and Remote Sensing Symposium, IGARSS, 2018
@inproceedings{bib_INTE_2018, AUTHOR = {LIPIKA AGARWAL, Rajan Krishnan Sundara}, TITLE = {INTEGRATING MSER INTO A FAST ICA APPROACH FOR IMPROVING BUILDING DETECTION ACCURACY}, BOOKTITLE = {International Geoscience and Remote Sensing Symposium}. YEAR = {2018}}
In this paper, a novel technique is presented to detect buildings from very high resolution satellite image. This work builds on the learning of ICA based building detection technique from the very high resolution (VHR) multispectral satellite images presented in [1]. The candidate building pixels obtained through ICA are used to extract maximally stable extremal regions (MSER) which are then filtered using geometric properties to obtain final potential buildings. The technique is aimed at reducing false detection at pixel-level and improving object-level performance of [1]. Combining the two works offers an unsupervised building detection technique which is robust towards size, shape, color, types of rooftops and shadows. A wider test image set consisting of 15 images of different dimensions are used to evaluate performance of the complete detection process. The combined technique achieves object-level precision and recall of 80.64% and 83.65% respectively.
MAARG: GEO-PROCESSING AND INFORMATION FUSION APPROACH FOR ROADmCONDITION AND SURFACE MONITORING
GANNU BHAVANA,Rajan Krishnan Sundara
Asian Conference on Remote Sensing, ACRS, 2018
@inproceedings{bib_MAAR_2018, AUTHOR = {GANNU BHAVANA, Rajan Krishnan Sundara}, TITLE = {MAARG: GEO-PROCESSING AND INFORMATION FUSION APPROACH FOR ROADmCONDITION AND SURFACE MONITORING}, BOOKTITLE = {Asian Conference on Remote Sensing}. YEAR = {2018}}
In many cities, it is difficult for authorities to build road monitoring systems that are cost-friendly and effective. The existing road monitoring systems include heavy machinery with sensors fitted which make them expensive for authorities to use for frequent monitoring. To address this challenge, we propose an innovative collaborative system for road condition monitoring leveraging the existing technologies by data fusion approach. The mobile application on smart-phones of geo-citizens coordinates the data collection and reports back to a central server for assimilation and further processing. Image data is processed to obtain the road surface while the accelerometer and GPS data is used for identifying road condition and location information. This data is then fused based on location and timestamp information and stored as segments in the database. As the data obtained is crowd-sourced, there are multiple tracks for the same segment of the road and this segmented approach allows for collating multiple user-provided information and validation. In case there is multiple track information available for a particular segment, a ranking model based on the number of collaborators is used to rank the status. The final database holds the information like segmentId, timestamp, GPS coordinates and status of the segment. This data serves as an input to the visualization service. The multiplicity of data points and anonymity of the users establishes robustness and crowd-sourced model ensures frequent and continuous coverage.
MAARG: GEO-PROCESSING AND INFORMATION FUSION APPROACH FOR ROAD CONDITION AND SURFACE MONITORING
GANNU BHAVANA,Rajan Krishnan Sundara
Asian Conference on Remote Sensing, ACRS, 2018
@inproceedings{bib_MAAR_2018, AUTHOR = {GANNU BHAVANA, Rajan Krishnan Sundara}, TITLE = {MAARG: GEO-PROCESSING AND INFORMATION FUSION APPROACH FOR ROAD CONDITION AND SURFACE MONITORING}, BOOKTITLE = {Asian Conference on Remote Sensing}. YEAR = {2018}}
In many cities, it is difficult for authorities to build road monitoring systems that are cost-friendly and effective. The existing road monitoring systems include heavy machinery with sensors fitted which make them expensive for authorities to use for frequent monitoring. To address this challenge, we propose an innovative collaborative system for road condition monitoring leveraging the existing technologies by data fusion approach. The mobile application on smart-phones of geo-citizens coordinates the data collection and reports back to a central server for assimilation and further processing. Image data is processed to obtain the road surface while the accelerometer and GPS data is used for identifying road condition and location information. This data is then fused based on location and timestamp information and stored as segments in the database. As the data obtained is crowd-sourced, there are multiple tracks for the same segment of the road and this segmented approach allows for collating multiple user-provided information and validation. In case there is multiple track information available for a particular segment, a ranking model based on the number of collaborators is used to rank the status. The final database holds the information like segmentId, timestamp, GPS coordinates and status of the segment. This data serves as an input to the visualization service. The multiplicity of data points and anonymity of the users establishes robustness and crowd-sourced model ensures frequent and continuous coverage.
MODELLING SHIFTING CULTIVATION AND CHANGES TO LAND USE IN BARAK VALLEY
JYOTI MISRA,Rajan Krishnan Sundara
Asian Conference on Remote Sensing, ACRS, 2018
@inproceedings{bib_MODE_2018, AUTHOR = {JYOTI MISRA, Rajan Krishnan Sundara}, TITLE = {MODELLING SHIFTING CULTIVATION AND CHANGES TO LAND USE IN BARAK VALLEY}, BOOKTITLE = {Asian Conference on Remote Sensing}. YEAR = {2018}}
Barak valley is an area in northeast part of India where traditionally practice of shifting cultivation is more prevalent than sedentary agriculture. In addition, population growth coupled with the geographic isolation of the area has further aggravated the decline of forest cover in the area. The three districts of Assam that cover the Barak valley show varied land covers and also has very different population densities. This paper attempts to model the land use changes in the Barak valley over a period of 1988 to 2005, using an agent-based model. Each district is modelled as an agent of change here to capture the interactions between the various land uses and to decide on the resource allocation over the district to be allotted to shifting cultivation. The model accounts for both economic as well as geographic factors, like access to infrastructure while making the land use decision on a year-on-year basis. The model considers the need for shifting cultivation area for both a staple crop - rice and a non-staple crop based on the demand against the supply estimated from irrigated and rain-fed crop regions. While the model has been tuned based on the data till 1997, the simulated model outcome of 2005 is evaluated against an existing remote sensing derived land use map of the region and it is found to achieve an accuracy of about 85%. While the aggregated results across the region show good concurrence, the randomness inherent in the choice of the shifting cultivation land limits the ability to predict the precise locations. The paper will discuss the challenges including scale issues in model application for a region as diverse as Barak.
IMPROVING PATHQUERY PERFORMANCE IN PGROUTING USING AMAPGENERALIZATIONAPPROACH
Sankepally Rohith Reddy,Rajan Krishnan Sundara
INTERNATIONAL SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING, ISPRS, 2018
@inproceedings{bib_IMPR_2018, AUTHOR = {Sankepally Rohith Reddy, Rajan Krishnan Sundara}, TITLE = {IMPROVING PATHQUERY PERFORMANCE IN PGROUTING USING AMAPGENERALIZATIONAPPROACH}, BOOKTITLE = {INTERNATIONAL SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING}. YEAR = {2018}}
pgRouting library provides functions to compute shortest path between any two points of a road network which is of great demand and also a topic of interest in the field of GIS, graph theory and transportation. To compute path in a road network, pgRouting functions process the entire road network which is a major bottleneck when it comes to routing in large road networks leading to the requirement of large server resources. A reduction/compression in the input network that is to be processed for path computation would improve the performance of pgRouting. In this study a map generalization based network model is proposed which extracts a significantly smaller subset of the road network aka skeleton which further used to divide the network into zones, that shall be selectively used in path computation. This results in processing a much smaller part of the network to compute path between any two points leading to an overall improvement in query performance of pgRouting when computing path, especially on large road networks. As part of assessment of this approach and its applicability to large road networks, the paper presents an in-depth analysis of the trade-offs between deviation in computed path and the performance gain in terms of space and time on road networks of varying sizes and topology to get a better understanding for both providing a sound proof of the utility of the proposed method and also to show its implementability within the current model of pgRouting or any other routing platforms.
Automatic Tree Identification and Diameter Estimation Using Single Scan Terrestrial Laser Scanner Data in Central Indian Forests
R. Suraj Reddy,Chandra Shekhar Jha,Rajan Krishnan Sundara
Journal of the Indian Society of Remote Sensing, JISRS, 2018
@inproceedings{bib_Auto_2018, AUTHOR = {R. Suraj Reddy, Chandra Shekhar Jha, Rajan Krishnan Sundara}, TITLE = {Automatic Tree Identification and Diameter Estimation Using Single Scan Terrestrial Laser Scanner Data in Central Indian Forests}, BOOKTITLE = {Journal of the Indian Society of Remote Sensing}. YEAR = {2018}}
Forest inventory parameters, primarily tree diameter and height, are required for several management and planning activities. Currently, Terrestrial Laser Scanning (TLS) is a promising technology in automated measurements of tree parameters using dense 3D point clouds. In comparison with conventional manual field inventory methods, TLS systems would supplement field data with detailed and relatively higher degree of accurate measurements and increased measurement frequency. Although, multiple scans from TLS captures more area, they are resource and time consuming to ensure proper co-registration between the scans. On the other hand, Single scans provide a fast and recording of the data but are often affected by occlusions between the trees. The current study evaluates potential of single scan TLS data to (1) develop an automatic method for tree stem identification and diameter estimation …
Automatic estimation of tree stem attributes using terrestrial laser scanning in central Indian dry deciduous forests
Suraj Reddy,Rakesh Fararoda,Chandra Shekhar Jha,Rajan Krishnan Sundara
Current Science, CURR SCI, 2018
@inproceedings{bib_Auto_2018, AUTHOR = {Suraj Reddy, Rakesh Fararoda, Chandra Shekhar Jha, Rajan Krishnan Sundara}, TITLE = {Automatic estimation of tree stem attributes using terrestrial laser scanning in central Indian dry deciduous forests}, BOOKTITLE = {Current Science}. YEAR = {2018}}
Forest inventories are critical for effective management of forest resources. Recently, the use of terrestrial laser scanning (TLS) to automatically extract forest inventory parameters at tree level (eg tree location, diameter at breast height (DBH) and height) has gained significant importance. TLS using both single-scan and multi-scan techniques, not only helps in detailed and accurate measurements of tree objects but also helps increase the measurement frequency. In the current study, we develop an automated solution to extract forest inventory parameters at individual tree level from TLS data by using random sample consensus (RANSAC)-based circle fitting algorithm. The method was evaluated on both single-and multiscan data by characterizing four circular plots of radius 20 m in dry deciduous forests of Betul, Madhya Pradesh (India). Over all the plots, tree detection rates of 75% and 97% were obtained using single-and multi-scan TLS data respectively. Tree detection rates were significantly affected by increase in distance from the scanner, in single-scan approach when compared to multi-scan approach. Field based DBH measurements correlated well using both single (R2= 0.96) and multiple scans (R2= 0.99). The DBH estimates from multi-scan TLS data resulted in low root-meansquare error (RMSE) of 2.2 cm compared to that of 4.1 cm using single-scan. Further, tree heights were extracted from TLS data and validated with selectively measured trees on field (R2= 0.98; N= 65). The RMSE of tree height was estimated to be 1.65 m. The current results show the potential use of TLS in automatically deriving forest inventory parameters with …
Improving Path Query Performance in PgRouting Using a Map Generalization Approach
Sankepally Rohith Reddy,Rajan Krishnan Sundara
International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, IAPRS, 2018
@inproceedings{bib_Impr_2018, AUTHOR = {Sankepally Rohith Reddy, Rajan Krishnan Sundara}, TITLE = {Improving Path Query Performance in PgRouting Using a Map Generalization Approach}, BOOKTITLE = {International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences}. YEAR = {2018}}
pgRouting library provides functions to compute shortest path between any two points of a road network which is of great demand and also a topic of interest in the field of GIS, graph theory and transportation. To compute path in a road network, pgRouting functions process the entire road network which is a major bottleneck when it comes to routing in large road networks leading to the requirement of large server resources. A reduction/compression in the input network that is to be processed for path computation would improve the performance of pgRouting. In this study a map generalization based network model is proposed which extracts a significantly smaller subset of the road network aka skeleton which further used to divide the network into zones, that shall be selectively used in path computation. This results in processing a much smaller part of the network to compute path between any two points leading to an overall improvement in query performance of pgRouting when computing path, especially on large road networks. As part of assessment of this approach and its applicability to large road networks, the paper presents an in-depth analysis of the trade-offs between deviation in computed path and the performance gain in terms of space and time on road networks of varying sizes and topology to get a better understanding for both providing a sound proof of the utility of the proposed method and also to show its implementability within the current model of pgRouting or any other routing platforms.
TOWARDS GENERATING SEMANTICALLY-RICH INDOORGML DATA FROM ARCHITECTURAL PLANS
SRISHTI SRIVASTAVA,NISHITH MAHESHWARI,Rajan Krishnan Sundara
International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, IAPRS, 2018
@inproceedings{bib_TOWA_2018, AUTHOR = {SRISHTI SRIVASTAVA, NISHITH MAHESHWARI, Rajan Krishnan Sundara}, TITLE = {TOWARDS GENERATING SEMANTICALLY-RICH INDOORGML DATA FROM ARCHITECTURAL PLANS}, BOOKTITLE = {International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences}. YEAR = {2018}}
Recent years has seen an increase in the work done on indoor data mapping and modeling. The standard data models provide different ways to store and access the indoor data but the way it is done is specific to the domain in which they are used. Although models like IFC, CityGML and IndoorGML provides rich functionality, the widespread availability of indoor data is not in these formats. This paper presents a step by step methodology to convert indoor building data of existing buildings, represented in architectural drawings into a topologically consistent and semantically rich indoor spatial model. The workflow presented consists of extracting relevant geometric entities from CAD drawings, assessing their topological relationships, using it to derive semantic information of spaces and making the data available in the form of IndoorGML. Since the current IndoorGML features lack the capability to store relevant semantic information, a semantic extension to IndoorGML is also proposed. The extraction of primitive spatial elements in rectilinear buildings like walls and doors are considered for the work presented in this paper. Development of a toolkit which implements this methodology in a seamless manner is work in progress and would incorporate extraction of complex spatial elements like staircases, ramps, curvilinear walls and windows, which is out of scope of the current work presented in this paper.
A Shape-based Approach to Spatio-Temporal Data Analysis using Satellite Imagery
DARPAN BAHETI,Rajan Krishnan Sundara
International Conference on Data Science and Advanced Analytics, DSAA, 2017
@inproceedings{bib_A_Sh_2017, AUTHOR = {DARPAN BAHETI, Rajan Krishnan Sundara}, TITLE = {A Shape-based Approach to Spatio-Temporal Data Analysis using Satellite Imagery }, BOOKTITLE = {International Conference on Data Science and Advanced Analytics}. YEAR = {2017}}
—Many socio-environmental aspects manifest themselves over space and time, interacting at varying scales of these dimensions. Satellite imagery, available repetitively over a region, provide important clues of these observations across these dimensions. But, also pose enormous challenges in terms of data processing, extracting significant patterns (indicating the underlying processes) and be able to further model them as scientific knowledge of the environmental process. In this paper, an effort has been made to propose a time-variant analysis method based on the shape characteristics of the vegetation response over time to help identify regions of significant changes. The study covers four agricultural-year periods between 2008 and 2012 over the district of West Godavari, in south of India. This approach shows that the effect of 2009 drought year on the agricultural practices vary spatially depending on the access to resources and the time-lag that manifests itself in such processes. In this study, we also find that nearly 80% of the region is well endowed and hence resilient to the climatic vagaries.
OBJECT BASED FUSION OF MULTI-SENSOR IMAGERY WHILE PRESERVINGSPECTRALLY SIGNIFICANT INFORMATION
MAYANK GOYAL,Rajan Krishnan Sundara
International Geoscience and Remote Sensing Symposium, IGARSS, 2017
@inproceedings{bib_OBJE_2017, AUTHOR = {MAYANK GOYAL, Rajan Krishnan Sundara}, TITLE = {OBJECT BASED FUSION OF MULTI-SENSOR IMAGERY WHILE PRESERVINGSPECTRALLY SIGNIFICANT INFORMATION}, BOOKTITLE = {International Geoscience and Remote Sensing Symposium}. YEAR = {2017}}
Data fusion is a prevalent method to extract the best combination of satellite images from different modalities - spectral,spatial, temporal. A new method of object-based fusion of high resolution multi spectral (MS) and panchromatic (PAN)images is proposed in this paper, which emphasizes on spectral characteristics preservation. It is a hybrid approach where individual objects detected in the images are considered for mapping data and information transfer is done on a per-pixel basis. In addition, the paper proposes a quantitative assessment measure to assess the spectral distortion of the fused outcomes. The quantitative results demonstrate that the pro-posed method is better in terms of preserving spectral characteristics as compared to other widely used methods such as Principle Component (PC) based fusion, Intensity Hue Saturation (IHS) based fusion and Color Normalization (CN) or Brovey
Stimulus of developmental projects to landscape dynamics in Uttara Kannada, Central WesternGhats
TV Ramachandra,SETTURU BHARATH,Rajan Krishnan Sundara,MD Subash Chandran
The Egyptian Journal of Remote Sensing and Space Science, EJRS, 2017
@inproceedings{bib_Stim_2017, AUTHOR = {TV Ramachandra, SETTURU BHARATH, Rajan Krishnan Sundara, MD Subash Chandran}, TITLE = {Stimulus of developmental projects to landscape dynamics in Uttara Kannada, Central WesternGhats}, BOOKTITLE = {The Egyptian Journal of Remote Sensing and Space Science}. YEAR = {2017}}
Unplanned large scale developmental projects in recent times have been causing alterations in land use land cover [LULC] at spatial and temporal scales with the substantial changes in the landscape. Uttara Kannada district has the distinction of having highest forest cover in India.Forest ecosystems in the district have witnessed major transformations due to industrialization during the post-independence. Impact of developmental activities during the post-independence era, is evident from barren hill tops due to deforestation. The analysis of temporal spatial data acquired through space borne sensors highlights the decline of evergreen-semi evergreen forest cover from 67.73% (1973) to 32.08% (2013). The ad-hoc approaches adopted in the implementation of developmental projects in the ecologically sensitive regions has impaired the ecosystem services affecting the people’s livelihood. The changes in the landscape structure with LULC changes have altered the functional abilities of an ecosystem evident from lowered hydrological yield, disappearing perennial streams, higher instances of human–animal conflicts, declined ecosystem goods, etc. About 64355 Ha of forest land is diverted for various non-forestry activities during the last four decades by the government apart from encroachment of 7072 Ha of forest area for agriculture, horticulture activities, etc. Encroachment of forest land has resulted in the land degradation leading to reduce dproductivity. Alterations in bio-geo chemical and hydrological cycles have necessitated the restoration of native forests in the region to ensure water and food security, and livelihood of the local people. Monitoring and visualization of landscape dynamics helps in evolving appropriate manage-ment strategies. LULC dynamics analyses considering agents (developmental projects) through fuzzy analytic hierarchy process (AHP) integrated with Markov cellular automata (CA) indicate the developmental projects such as West Coast Paper Mill (WCPM) and Kaiga Nuclear PowerHouse (KNPH) will lead to further fragmentation of forests with spurt in urbanization by 2022.This necessitates framing appropriate policy measures to sustain natural resources focusing on the landscape’s ecological, hydrological, economic and social factors.
Geo-Visualization of land cover transitions in Karwar region of Central Western Ghats
SETTURU BHARATH,Rajan Krishnan Sundara,Ramachandra T V
WATER URBANISM AND INFRASTRUCTURE DEVELOPMENT IN ECO-SENSITIVE ZONES, WIEZ, 2017
@inproceedings{bib_Geo-_2017, AUTHOR = {SETTURU BHARATH, Rajan Krishnan Sundara, Ramachandra T V}, TITLE = {Geo-Visualization of land cover transitions in Karwar region of Central Western Ghats}, BOOKTITLE = {WATER URBANISM AND INFRASTRUCTURE DEVELOPMENT IN ECO-SENSITIVE ZONES}. YEAR = {2017}}
Pervasive large scale land cover land use (LULC) changes leading to deforestation have enhanced concentrations of carbon dioxide in the atmosphere and significantly impacted the regional water cycle and bio-geo chemical cycles. This neceissates assessment of current level of transitions and likely future transitions for an effective decision making towards sustainable management of natural resources. Karwar taluk of Central Western Ghats has been experiencing rapid landscape changes due to unplanned developmental activities and the consequences were evident form frequent occurrence of landslides with the loss of life and property. Analysis of land cover transitions based on temporal spatial data helps more efficient land management policies. The present study aims to visualize and forecast forest land status (2000-2026) by exploring the effects of various factors such as protected areas, slope, waterbodies, proximity to roads, industries and urban centers through agent based model on land use change. The land use change inducing factors are normalized and relative weights were generated through fuzzyfication and Multi criteria Evaluation (MCE). The spatial patterns were quantified using metrics such as Number of Patches (NP), the Edge Density (ED) and Area-Weighted Mean Patch Fractal Dimension (AWMPFD). The forest cover has reduced from 73.59 % (2000) to 67.32% (2016) with an increase in built-up from 3.17 (2000) to 8.65% (2016). Forest cover in 2024 would be about 60.44% with increase in built-up area 14.79%. Unplanned developmental activities with the increasing trends of urbanisation are the prime reasons for disasters such as landslides, water scarcity, etc. The technique has provided insights to unsustainable land cover transitions in the region for an integrated and holistic path of development to sustain natural resources while mitigating human made disasters.
LOCALIZED SEGMENT BASED PROCESSING FOR AUTOMATIC BUILDING EXTRACTION FROM LiDAR DATA
GAURAV PARIDA,Rajan Krishnan Sundara
INTERNATIONAL SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING, ISPRS, 2017
@inproceedings{bib_LOCA_2017, AUTHOR = {GAURAV PARIDA, Rajan Krishnan Sundara}, TITLE = {LOCALIZED SEGMENT BASED PROCESSING FOR AUTOMATIC BUILDING EXTRACTION FROM LiDAR DATA}, BOOKTITLE = {INTERNATIONAL SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING}. YEAR = {2017}}
The current methods of object segmentation and extraction and classification of aerial LiDAR data is manual and tedious task. This work proposes a technique for object segmentation out of LiDAR data. A bottom-up geometric rule based approach was used initially to devise a way to segment buildings out of the LiDAR datasets. For curved wall surfaces, comparison of localized surface normals was done to segment buildings. The algorithm has been applied to both synthetic datasets as well as real world dataset of Vaihingen, Germany. Preliminary results show successful segmentation of the buildings objects from a given scene in case of synthetic datasets and promissory results in case of real world data. The advantages of the proposed work is non-dependence on any other form of data required except LiDAR. It is an unsupervised method of building segmentation, thus requires no model training as seen in supervised techniques. It focuses on extracting the walls of the buildings to construct the footprint, rather than focussing on roof. The focus on extracting the wall to reconstruct the buildings from a LiDAR scene is crux of the method proposed. The current segmentation approach can be used to get 2D footprints of the buildings, with further scope to generate 3D models. Thus, the proposed method can be used as a tool to get footprints of buildings in urban landscapes, helping in urban planning and the smart cities endeavour.
Analyzing the performance of NoSQL vs. SQL databases for Spatial and Aggregate queries
SARTHAK AGARWAL,Rajan Krishnan Sundara
International Conference for Free and Open Source Software for Geospatial, FOSS4G, 2017
@inproceedings{bib_Anal_2017, AUTHOR = {SARTHAK AGARWAL, Rajan Krishnan Sundara}, TITLE = {Analyzing the performance of NoSQL vs. SQL databases for Spatial and Aggregate queries}, BOOKTITLE = {International Conference for Free and Open Source Software for Geospatial}. YEAR = {2017}}
Relational databases have been around for a long time and spatial databases have exploited this feature for close to two decades. The recent past has seen the development of NoSQL non-relational databases, which are now being adopted for spatial object storage and handling, too. While SQL databases face scalability and agility challenges and fail to take the advantage of the cheap memory and processing power available these days, NoSQL databases can handle the rise in the data storage and frequency at which it is accessed and processed - which are essential features needed in geospatial scenarios, which do not deal with a fixed schema(geometry) and fixed data size. This paper attempts to evaluate the performance of \ an existing NoSQL database ’MongoDB’ with its inbuilt spatial functions with that of a SQL database with spatial extension ’PostGIS’ for two problems spatial and aggregate queries, across a range of datasets, with varying features counts. All the data in the analysis was processed In-memory and no secondary memory was used. Initial results suggest that MongoDB performs better by an average factor of 10x-25x which increases exponentially as the data size increases in both indexed and non-indexed operations. Given these results, NoSQL databases may be better suited for simultaneous multiple-user query systems including Web-GIS and mobile-GIS. Further studies are required to understand the full potential of NoSQL databases across various geometries and spatial query types.
FOSS based Interactive Spatial Temporal Analytical Tool: a GeoBI case study of retail data
NEHA MILIND PANDE,Rajan Krishnan Sundara
Asian Conference on Remote Sensing, ACRS, 2017
@inproceedings{bib_FOSS_2017, AUTHOR = {NEHA MILIND PANDE, Rajan Krishnan Sundara}, TITLE = {FOSS based Interactive Spatial Temporal Analytical Tool: a GeoBI case study of retail data}, BOOKTITLE = {Asian Conference on Remote Sensing}. YEAR = {2017}}
Online WebGIS tools and services have become widely available in the recent past to visualize and query geospatial data. These are primarily spatial visualization of aggregated attribute data across various spatial scales or geo-hierarchy levels. Most of these tools lack the temporal profile of the data. Also, these are server centric. In the era of big data, the user data has various attributes collected over time across geo graphic detail. Also user may like to do visual analysis across these attributes at various levels of aggregation. Such server centric approaches give limited maneuverability to explore the data and discover hidden patterns and insights in data. This paper presents a Spatial-OLAP tool that has been developed over existing Open Source geospatial and database tools. It computes data aggregation across user-defined geo hierarchy with dynamic visualizations in a defined framework. Parameters in menu options are not pre-computed but added on the fly based on user-given data. The tool enriches user experience by combining additional data-aggregation functions over spatial too lkits like open layers and allowing user to zoom to a set of temporal charts for a more extensive analysis of data across space and time. To show the utility of this tool, a GeoBI case study was done on a large online retail data of three products sold in different countries during January 2009. The tool helped quickly identify the top five countries with maximum sales and the payment options in decreasing order of popularity. Also, relationships between quantity of product sold across the geographic regions was also extracted. As Online retailers need to draw insights from data to understand their customers across regions, we hope this tool can benefit them in finding such answers - what their popular products are, identifying regions where there is a demand for these, understanding regional preferences and many others.
FOSS4G Modelling of forest cover transitions with Kaiga nuclear plant
SETTURU BHARATH,Rajan Krishnan Sundara,Ramachandra T V
International Conference for Free and Open Source Software for Geospatial, FOSS4G, 2017
@inproceedings{bib_FOSS_2017, AUTHOR = {SETTURU BHARATH, Rajan Krishnan Sundara, Ramachandra T V}, TITLE = {FOSS4G Modelling of forest cover transitions with Kaiga nuclear plant}, BOOKTITLE = {International Conference for Free and Open Source Software for Geospatial}. YEAR = {2017}}
Drastic land use land cover changes (LULC) in forest landscape alters the functional ability of forest ecosystem, evident from decline in quantity and duration of water flow in streams, loss of carbon sequestration potential, etc. affecting the local livelihood, while inducing changes in the climate. This necessitates inventorying, mapping and monitoring of LULC dynamics to evolve appropriate location specific mitigation strategies. The LULC dynamics can be assessed effectively through free and open source software using temporal remote sensing data and modeling and forecasting techniques. The current research work aims to identify spatio-temporal land use changes from 2007-2016 in Kaiga Nuclear Power house (NPH) and its environs with the help of GRASS GIS (a robust free and open source software). Land uses in 2022 is predicted through Cellular Automata–Markov Chain model with the knowledge of land use transitions during the four decades. Validation of the model is done using Kappa indices, which shows good accuracy due to agreement of the predicated and actual land uses. The region has lost forest cover from 23 to 16.81 % (2016-2022) with an increase of built-up are of 42.01 to 46.81 % (2016-2022). The land use modeling, planning and policy by implying ecological science perspectives can improve understanding of ecological processes. The recent study reports of higher instances of cancer in humans with the functioning of NPH during the last decade. Any further expansion of NPH area would have serious ecological implications due to loss of vast tracts of forests, farmlands and would threaten the health and livelihood of local population.
Modelling the forest transition in Central Western Ghats, India
T. V. Ramachandra,SETTURU BHARATH,Rajan Krishnan Sundara,M. D. Subash Chandran
Spatial Information Research, SpIR, 2017
@inproceedings{bib_Mode_2017, AUTHOR = {T. V. Ramachandra, SETTURU BHARATH, Rajan Krishnan Sundara, M. D. Subash Chandran}, TITLE = {Modelling the forest transition in Central Western Ghats, India}, BOOKTITLE = {Spatial Information Research}. YEAR = {2017}}
The Western Ghats forms an important watershed for the entire peninsular India, being the source of 37 west flowing rivers and three major east flowing rivers and their numerous tributaries. However, deforestation due to large scale land cover changes has affected the water sustenance in the region evident from the quantity and duration of water availability during post monsoon period. Land use Land cover changes accelerated by unplanned anthropogenic activities have been the prime mover of global warming and consequent changes in the climate. This necessitates appropriate resource management with an understanding of drivers. Geo-visualization of landscape transitions considering the influential agents will aid in formulating strategies to mitigate global warming. Uttara Kannada district in the Central Western Ghats has the distinction of having highest forest cover in the country and this region is now experiencing rapid forest cover changes. Factors inducing changes in the land cover are normalized through fuzzyfication, considered for Multi criteria Evaluation using Analytical Hierarchy Process (AHP) under high protection and low protection scenarios. Likely land use transitions by 2022 across zones based on transitions during 2004–2007, 2007–2010, 2010–2013 was done through cellular automata and Markov chain process (MC). The analyses highlight the loss of forest cover by 66.55–56.76% by 2022 in the coastal zone with escalating population density. Similar situation of 65.98–55.62% decline in Sahyadri region is noticed with execution of dams, hydroelectric projects and monoculture plantations. Lower transitions as compared with the second scenario highlights regulatory framework’s role in protection. However, forests in plain region show loss of 27.38–11.09% in both scenarios due to population pressure and market induced land cover changes. This necessitates policy interventions by the federal government to mitigateforest loss towards sustainable development.
QUERY SUPPORT FOR GMZ.
AYUSH KHANDELWAL,Rajan Krishnan Sundara
INTERNATIONAL SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING, ISPRS, 2017
@inproceedings{bib_QUER_2017, AUTHOR = {AYUSH KHANDELWAL, Rajan Krishnan Sundara}, TITLE = {QUERY SUPPORT FOR GMZ.}, BOOKTITLE = {INTERNATIONAL SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING}. YEAR = {2017}}
Generic text-based compression models are simple and fast but there are two issues that needs to be addressed. They cannot leverage the structure that exists in data to achieve better compression and there is an unnecessary decompression step before the user can actually use the data. To address these issues, we came up with GMZ, a lossless compression model aimed at achieving high compression ratios. The decision to design GMZ (Khandelwal and Rajan, 2017) exclusively for GML's Simple Features Profile (SFP) seems fair because of the high use of SFP in WFS and that it facilitates high optimisation of the compression model. This is an extension of our work on GMZ. In a typical server-client model such as Web Feature Service, the server is the primary creator and provider of GML, and therefore, requires compression and query capabilities. On the other hand, the client is the primary consumer of GML, and therefore, requires decompression and visualisation capabilities. In the first part of our work, we demonstrated compression using a python script that can be plugged in a server architecture, and decompression and visualisation in a web browser using a Firefox addon. The focus of this work is to develop the already existing tools to provide query capability to server. Our model provides the ability to decompress individual features in isolation, which is an essential requirement for realising query in compressed state. We construct an R-Tree index for spatial data and a custom index for non-spatial data and store these in a separate index file to prevent alter - ing the compression model. This facilitates independent use of compressed GMZ file where index can be constructed when required. The focus of this work is the bounding-box or range query commonly used in webGIS with provision for other spatial and non-spatial queries. The decrement in compression ratios due to the new index file is in the range of 1-3 percent which is trivial considering the benefits of querying in compressed state. With around 75% average compression of the original data, query support in compressed state and decompression support in the browser, GMZ can be a good alternative to GML for WFS-like services.
LSIVIEWER 2.0 - A CLIENT-ORIENTED ONLINE VISUALIZATION TOOL FOR GEOSPATIAL VECTOR DATA
KONDETI MANIKANTA PURUSHOTHAM,Rajan Krishnan Sundara
ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, ISPRS-APRSS, 2017
@inproceedings{bib_LSIV_2017, AUTHOR = {KONDETI MANIKANTA PURUSHOTHAM, Rajan Krishnan Sundara}, TITLE = {LSIVIEWER 2.0 - A CLIENT-ORIENTED ONLINE VISUALIZATION TOOL FOR GEOSPATIAL VECTOR DATA}, BOOKTITLE = {ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences}. YEAR = {2017}}
Geospatial data visualization systems have been predominantly through applications that are installed and run in a desktop environment. Over the last decade, with the advent of web technologies and its adoption by Geospatial community, the server-client model for data handling, data rendering and visualization respectively has been the most prevalent approach in Web-GIS. While the client devices have become functionally more powerful over the recent years, the above model has largely ignored it and is still in a mode of serverdominant computing paradigm. In this paper, an attempt has been made to develop and demonstrate LSIViewer - a simple, easy-to-use and robust online geospatial data visualisation system for the user’s own data that harness the client’s capabilities for data rendering and user-interactive styling, with a reduced load on the server. The developed system can support multiple geospatial vector formats and can be integrated with other web-based systems like WMS, WFS, etc. The technology stack used to build this system is Node.js on the server side and HTML5 Canvas and JavaScript on the client side. Various tests run on a range of vector datasets, upto 35MB, showed that the time taken to render the vector data using LSIViewer is comparable to a desktop GIS application, QGIS, over an identical system.
GMZ: A GML COMPRESSION MODEL FOR WEBGIS
AYUSH KHANDELWAL,Rajan Krishnan Sundara
ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, ISPRS-APRSS, 2017
@inproceedings{bib_GMZ:_2017, AUTHOR = {AYUSH KHANDELWAL, Rajan Krishnan Sundara}, TITLE = {GMZ: A GML COMPRESSION MODEL FOR WEBGIS}, BOOKTITLE = {ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences}. YEAR = {2017}}
Geography markup language (GML) is an XML specification for expressing geographical features. Defined by Open Geospatial Consortium (OGC), it is widely used for storage and transmission of maps over the Internet. XML schemas provide the convenience to define custom features profiles in GML for specific needs as seen in widely popular cityGML, simple features profile, coverage, etc. Simple features profile (SFP) is a simpler subset of GML profile with support for point, line and polygon geometries. SFP has been constructed to make sure it covers most commonly used GML geometries. Web Feature Service (WFS) serves query results in SFP by default. But it falls short of being an ideal choice due to its high verbosity and size-heavy nature, which provides immense scope for compression. GMZ is a lossless compression model developed to work for SFP compliant GML files. Our experiments indicate GMZ achieves reasonably good compression ratios and can be useful in WebGIS based applications.
Geo-visualisation of landscape dynamics in Sirsi and Haliyal forest divisions of Central Western Ghats
SETTURU BHARATH,Rajan Krishnan Sundara,Ramachandra T V
National Conference on Challenges of Civil Engineering Innovations, NCCCEI, 2016
@inproceedings{bib_Geo-_2016, AUTHOR = {SETTURU BHARATH, Rajan Krishnan Sundara, Ramachandra T V}, TITLE = {Geo-visualisation of landscape dynamics in Sirsi and Haliyal forest divisions of Central Western Ghats}, BOOKTITLE = {National Conference on Challenges of Civil Engineering Innovations}. YEAR = {2016}}
Land use land cover (LULC) information at temporal scale has become necessary in the analysis of environmental progression, resource management and planning. Uncontrolled expansion of human activities are playing an important role in global land use land cover change affecting ecology, climate change, soil erosion and hydrological regime. Visualizing and quantifying future changes of land use is also essential in high forested regions such as Uttara Kannada to ensure appropriate management practices and policies for maintaining the ecosystem services provided by forests. The landscape transition has accounted across five forest jurisdictions from 2004 to 2013 and visaulised further by non-agent based modeling for 2026. Cellular automata (CA), Markov chain model approaches has evolved as a promising tools of visualisation helps in complicated decision making process to understand the neighboring effects of land use activities. The Haliyal division has lost major cover from 2004 (55.48%) to 2013 (45.29%) due to increase in population pressure, unplanned developmental activities and conversion of forest area into
A SEMANTIC MODEL TO DEFINE INDOOR SPACE IN CONTEXT OF EMERGENCY EVACUATION
NISHITH MAHESHWARI,Rajan Krishnan Sundara
ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, ISPRS-APRSS, 2016
@inproceedings{bib_A_SE_2016, AUTHOR = {NISHITH MAHESHWARI, Rajan Krishnan Sundara}, TITLE = {A SEMANTIC MODEL TO DEFINE INDOOR SPACE IN CONTEXT OF EMERGENCY EVACUATION}, BOOKTITLE = {ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences}. YEAR = {2016}}
There have been various ways in which the indoor space of a building has been defined. In most of the cases the models have specific purpose on which they focus such as facility management, visualisation or navigation. The focus of our work is to define semantics of a model which can incorporate different aspects of the space within a building without losing any information provided by the data model. In this paper we have suggested a model which defines indoor space in terms of semantic and syntactic features. Each feature belongs to a particular class and based on the class, has a set of properties associated with it. The purpose is to capture properties like geometry, topology and semantic information like name, function and capacity of the space from a real world data model. The features which define the space are determined using the geometric information and the classes are assigned based on the relationships like connectivity, openings and function of the space. The ontology of the classes of the feature set defined will be discussed in the paper.
UNDERSTANDING THE BEHAVIOUR OF CONTAMINATION SPREAD IN NAGARJUNA SAGAR RESERVOIR USING TEMPORAL LANDSAT DATA.
K TARUN TEJA,Rajan Krishnan Sundara
ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, ISPRS-APRSS, 2016
@inproceedings{bib_UNDE_2016, AUTHOR = {K TARUN TEJA, Rajan Krishnan Sundara}, TITLE = {UNDERSTANDING THE BEHAVIOUR OF CONTAMINATION SPREAD IN NAGARJUNA SAGAR RESERVOIR USING TEMPORAL LANDSAT DATA.}, BOOKTITLE = {ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences}. YEAR = {2016}}
LANDSAT images are used to identify organic contaminants in water bodies, but, there is no enough evidence in present literature that LANDSAT is also good in identifying a mixture of organic and mineral contaminants such as agricultural waste. The focus of this paper is to evaluate the effectiveness of LANDSAT imagery to identify organic and mineral contamination (OMC) and to identify spread extent variations of pollution over the season/year in the Nagarjuna Sagar (NS) reservoir using only satellite images. A new band combination is proposed in order to detect OMC, because existing formulae based on band ratio proved to be inadequate in detecting the contamination in NS. Difference in reflectance values of Red and Green channel of an image helps clearly distinguish clear water from OMC water. This procedure was applied over LANDSAT data of the calendar years 2008, 2014 and 2015 to understand the contamination spread pattern through the reservoir. Results show that contamination is following a similar pattern over these calendar years. In January contamination starts at inlets and by May contamination spreads to almost 90% of the reservoir when the total area of water spread is also reduced by half. Contamination spread is low during the monsoonal period of June to September due to heavy inflow and heavy outflow of waters from NS reservoir. Post monsoon NS is contaminated again because of heavy inflow of runoffs from neighboring land use and limited water outflow. This contamination spread pattern matches the agricultural seasons and fertilizer application pattern in this region, indicating that agricultural use of fertilizers could be one of the primary causes of contamination for this waterbody.
Performance analysis of MongoDB versus PostGIS/PostGreSQL databases for line intersection and point containment spatial queries
SARTHAK AGARWAL,Rajan Krishnan Sundara
Spatial Information Research, SpIR, 2016
@inproceedings{bib_Perf_2016, AUTHOR = {SARTHAK AGARWAL, Rajan Krishnan Sundara}, TITLE = {Performance analysis of MongoDB versus PostGIS/PostGreSQL databases for line intersection and point containment spatial queries}, BOOKTITLE = {Spatial Information Research}. YEAR = {2016}}
Relational databases have been around for a long time, and Spatial databases have exploited this feature for close to two decades. The recent past has seen the development of NoSQL non-relational databases, which are now being adopted for spatial object storage and handling too. Moreover, this is gaining ground in the context of increased shift towards Geospatial Web Services on both the Web and mobile platforms especially in the user-centric services, where there is a need to improve the query response time. This paper attempts to evaluate the performance of an existing NoSQL database ‘MongoDB’ with its inbuilt spatial functions with that of an SQL database with spatial extension ‘PostGIS’ for two fundamental spatial problems—line intersection and point containment problem, across a range of datasets, with varying feature counts. Given these results, NoSQL databases may be better suited for simultaneous multiple-user query systems including Web-GIS and mobile-GIS. Further studies are required to understand the full potential of NoSQL databases across various geometries and spatial query types.
Semi-automatic extraction of large and moderate buildings from very high-resolution satellite imagery using active contour model
BYPINA SANDEEP KUMAR,Rajan Krishnan Sundara
International Geoscience and Remote Sensing Symposium, IGARSS, 2015
@inproceedings{bib_Semi_2015, AUTHOR = {BYPINA SANDEEP KUMAR, Rajan Krishnan Sundara}, TITLE = {Semi-automatic extraction of large and moderate buildings from very high-resolution satellite imagery using active contour model}, BOOKTITLE = {International Geoscience and Remote Sensing Symposium}. YEAR = {2015}}
The traditional pixel-based classification totally relies on spectral information and neglects the spatial information. These methods when applied on very high-resolution imagery get confused because of the increased variability implicit within the data and thus leads to lower classification accuracies.The object-based image analysis (OBIA) is advantageous to deal with objects that are composed of homogeneous pixels.This paper aims at automatically extracting buildings from very high-resolution satellite imagery using Object Based Image Analysis(OBIA).The algorithm uses an active contour model called chan-vese segmentation to create objects from the image. Objects representing vegetation or trees are removed by subtracting NDVI mask from the segmented output. The detected objects are further filtered based on regional properties like minimum area, width of object etc. The results are promising with 74-77%of the buildings getting detected as objects
A CHAN VESE BASED METHOD OF TEXTURE EXTRACTION FOR AUTOMATEDTEXTURE DRAPING OF 3D GEOSPATIAL OBJECTS
VISHAL TIWARI,Rajan Krishnan Sundara
International Geoscience and Remote Sensing Symposium, IGARSS, 2015
@inproceedings{bib_A_CH_2015, AUTHOR = {VISHAL TIWARI, Rajan Krishnan Sundara}, TITLE = {A CHAN VESE BASED METHOD OF TEXTURE EXTRACTION FOR AUTOMATEDTEXTURE DRAPING OF 3D GEOSPATIAL OBJECTS}, BOOKTITLE = {International Geoscience and Remote Sensing Symposium}. YEAR = {2015}}
We have addressed the problem of visualization of vector building data on top of a terrain. It tries to make it more realistic by automated draping of building textures from geo-tagged images which are captured from a cell phone camera with a built-in GPS. We use the properties of the images to tag them to the corresponding 2D polygon footprint by using the camera pose, and the cameras position to automate our process. The elevation data captured from NASAs SRTM project, GTOPO, etc can be used to render 3D terrains using existing GPU based Level of detail(LOD) algorithms[1] [2][3]. The vector data of buildings along with their height at-tributes can be obtained from various Lidar based algorithmsor from DEM data[4] [5]. A scene graph is used as the data structure which is used to render these vector based graphics models
Exploring the impact of road traffic impedance and built environment for vulnerability mapping of evacuation areas
RAJESH CHATURVEDI,Rajan Krishnan Sundara
International Space Syntax Symposium, ISSS, 2015
@inproceedings{bib_Expl_2015, AUTHOR = {RAJESH CHATURVEDI, Rajan Krishnan Sundara}, TITLE = {Exploring the impact of road traffic impedance and built environment for vulnerability mapping of evacuation areas}, BOOKTITLE = {International Space Syntax Symposium}. YEAR = {2015}}
This study identifies spaces vulnerable to a disaster in terms of impedance offered to evacuation. Evacuation for spaces is evidently dependent on combination of several spatia and demographic features. In order to develop an evacuation model or prioritize regions for special attention during calamity, it is necessary to understand interactions and interdependencies of numerous factors. In this study,we address vulnerability issue in terms of potential difficulties in evacuating a region from a spatial perspective. Arrangement of built-up areas and interactions of neighborhoods are studied based on topological as well as metric distances between them. We couple building to building (point to point) accessibility considering metric distances & their respective on-ground areas with space syntax based axial analysis on urban streets.The approach creates multidimensional feature vector on top of buildings layer.Feature vectors consist of building on-ground areas,their reach,betweenness, and local integration & choice values of segments adjacent to which they are located for a metric radius of 500 meters and topological radius R10. Further, we use bivariate Local Indicators of Spatial Association(LISA) to identify the clusters and conclusively carry out knowledge based denomination of the areas in terms of their vulnerability. The study reveals that road segments offering highest traffic impedance for planned grid like arrangements are parallel, of equal length and in close vicinity of each other whereas for non – grid wards are rather scattered. The clusters of buildings located on roads with lower accessibility are significantly less in number as well as smaller sized for grid like symmetries compared to non–grid arrangements. The evaluation of areas from vulnerability perspective carried out in this study can form the basis of a generalized decision support system– framework to identify, rank and prioritize both the current and future space planning and emergency response
Analysing the Performance of NoSQL vs SQL Databases with Respect to Routing Algorithms
SARTHAK AGARWAL,Rajan Krishnan Sundara
International Conference for Free and Open Source Software for Geospatial, FOSS4G, 2015
@inproceedings{bib_Anal_2015, AUTHOR = {SARTHAK AGARWAL, Rajan Krishnan Sundara}, TITLE = {Analysing the Performance of NoSQL vs SQL Databases with Respect to Routing Algorithms}, BOOKTITLE = {International Conference for Free and Open Source Software for Geospatial}. YEAR = {2015}}
With the increased shift towards GeoSpatial Web Services on both the Web and mobile platforms especially in the usercentric services, there is a need to improve the query response time. The traditional routing algorithm requires server to process the query and send the results to a client but here we are focussing on query processing within the client itself. This paper attempts to evaluate the performance of an existing NoSQL database and SQL database with respect to routing algorithm and evaluate whether or not we can deploy the computations on the client system only. While SQL databases face the challenges of scalability and agility and are unable to take the advantage of the abundant memory and processing power available these days, NoSQL databases are able to use some of these features to their advantage. The nonrelational databases are more suited for handling the dynamic rise in the data storage and the increased frequency of data accessibility. For this comparative study, MongoDB is the NoSQL engine while the PostgreSQL is the chosen SQL engine. The dataset is a synthetic dataset of road network with several nodes and we find the distance between source and destination using various algorithms. As a part of paper The implementation we are planning on using pgRouting for the analysis which currently uses PostgreSQL at the backend and implements almost all the routing algorithms essential in practical scenarios. We have currently analyzed the performance of NoSQL databases for various spatial queries and have extended that work to routing.
3D Visualization of City GML Building Data on World Wind
VISHAL TIWARI,Rajan Krishnan Sundara
International Conference for Free and Open Source Software for Geospatial, FOSS4G, 2015
@inproceedings{bib_3D_V_2015, AUTHOR = {VISHAL TIWARI, Rajan Krishnan Sundara}, TITLE = {3D Visualization of City GML Building Data on World Wind}, BOOKTITLE = {International Conference for Free and Open Source Software for Geospatial}. YEAR = {2015}}
Large area cityGML visualization currently deals with its conversion to another model such as VRML, X3D or KML data format which can be visualized using various visualization tools like Google Earth, VTP, Cesium, NASAs World Wind, etc. But these conversion to VRML or X3D, leads to a loss of the geographical information, geometries, which restricts the aspect of geospatial analysis, in addition to adding an extra preprocessing step. One of the tool that supports 3D visualization of cityGML data is Aristoteles developed by the Institute for Cartography and Geoinformation. But it lacks features like move around camera controls, projecting the coordinates from the cityGML CRS, and underneath terrain rendering. On the other hand virtual globes like NASAs world wind or cesium globe, are powerful enough to harness the power of the systems GPU to render large amount of geometries and DEM data to generate terrains, give scene controls, etc, but doesn't give support for visualization of cityGML data in its current form. Thus there is a need to integrate the virtual globe technology and the visualization functionality of tools like Aristoteles, so as to render buildings on top of terrain and give a more realistic view of the environment.
Meta-analysis of EIA public hearings in the state of Gujarat, India: its role versus the goal of environmental management
N VENKATA SAINATH,Rajan Krishnan Sundara
Impact Assessment and Project Appraisal, IAPA, 2015
@inproceedings{bib_Meta_2015, AUTHOR = {N VENKATA SAINATH, Rajan Krishnan Sundara}, TITLE = {Meta-analysis of EIA public hearings in the state of Gujarat, India: its role versus the goal of environmental management}, BOOKTITLE = {Impact Assessment and Project Appraisal}. YEAR = {2015}}
Public consultation is an important decision-aiding process in environmental impact assessment (EIA) and aids in building up consensus between various stakeholders, primarily the local public. In this meta-analysis, proceedings of 100 public hearings (PHs) recorded in the Indian state of Gujarat were analysed for the views of local public between environmental issues and others while an industry is being set up across five sectors – bulk drug and drug intermediates, cement, highway projects, oil and gas exploration and thermal power plants. The analysis shows that environmental issues are only 33% of the total issues raised, while socio-economic, infrastructure, PH process, track record and other general issues cover 21%, 13%, 2%, 12% and 19%, respectively. This implies that irrespective of sector or project local socio-economic and developmental concerns outweigh environmental issues and the current PH process is not able to get the appropriate inputs and insights from the stakeholders in improving the environmental decision-making. In light of these, some alternatives for strengthening the EIA-PH process is proposed in the paper
MAARGHA: A Prototype System for Road Condition and Surface Type Estimation by Fusing Multi-Sensor Data
DEEPAK R,GANNU BHAVANA,Rajan Krishnan Sundara
ISPRS International Journal of Geo-Information, ISPRS- IJG, 2015
@inproceedings{bib_MAAR_2015, AUTHOR = {DEEPAK R, GANNU BHAVANA, Rajan Krishnan Sundara}, TITLE = {MAARGHA: A Prototype System for Road Condition and Surface Type Estimation by Fusing Multi-Sensor Data}, BOOKTITLE = {ISPRS International Journal of Geo-Information}. YEAR = {2015}}
Road infrastructure in countries like India is expanding at a rapid pace and is becoming increasingly difficult for authorities to identify and fix the bad roads in time. Current Geographical Information Systems (GIS) lack information about on-road features like road surface type, speed breakers and dynamic attribute data like the road quality. Hence there is a need to build road monitoring systems capable of collecting such information periodically. Limitations of satellite imagery with respect to the resolution and availability, makes road monitoring primarily an on-field activity. Monitoring is currently performed using special vehicles that are fitted with expensive laser scanners and need skilled resource besides providing only very low coverage. Hence such systems are not suitable for continuous road monitoring. Cheaper alternative systems using sensors like accelerometer and GPS (Global Positioning System) exists but they are not equipped to achieve higher information levels. This paper presents a prototype system MAARGHA (MAARGHA in Sanskrit language means an eternal path to solution), which demonstrates that it can overcome the disadvantages of the existing systems by fusing multi-sensory data like camera image, accelerometer data and GPS trajectory at an information level, apart from providing additional road information like road surface type. MAARGHA has been tested across different road conditions and sensor data characteristics to assess its potential applications in real world scenarios. The developed system achieves higher information levels when compared to state of the art road condition estimation systems like Roadroid. The system performance in road surface type classification is dependent on the local environmental conditions at the time of imaging. In our study, the road surface type classification accuracy reached 100% for datasets with near ideal environmental conditions and dropped down to 60% for datasets with shadows and obstacles
FAST ICA BASED ALGORITHM FOR BUILDING DETECTION FROM VHR IMAGERY
LIPIKA AGARWAL,Rajan Krishnan Sundara
International Geoscience and Remote Sensing Symposium, IGARSS, 2015
@inproceedings{bib_FAST_2015, AUTHOR = {LIPIKA AGARWAL, Rajan Krishnan Sundara}, TITLE = {FAST ICA BASED ALGORITHM FOR BUILDING DETECTION FROM VHR IMAGERY}, BOOKTITLE = {International Geoscience and Remote Sensing Symposium}. YEAR = {2015}}
In the recent past there is increased interest in detection and extraction of buildings using object based approaches on given high to very high resolution imagery. In this paper, we introduce a new unsupervised approach to detect buildings from the very high resolution (VHR) multispectral satellite image. Independent component analysis (ICA) followed by Otsu thresholding is used for extraction of multicoloured buildings of diversified size and shape. QuickBird imageof Legaspi city has been used to analyze the technique and detection results obtained from three subset images indicate average detection percentage of 84.35 % along with 38.19 % branch factor value. Object-level evaluation results give 72.48% for fully detected buildings count
Inventorying, Mapping and Monitoring of Mangroves towards Sustainable Management of West Coast, India
Prakash N Mesta,Bharath Setturu, Subash Chandran MD,Rajan Krishnan Sundara,TV Ramachandra
Journal of Geophysics and Remote Sensing, GRS, 2014
@inproceedings{bib_Inve_2014, AUTHOR = {Prakash N Mesta, Bharath Setturu, Subash Chandran MD, Rajan Krishnan Sundara, TV Ramachandra}, TITLE = {Inventorying, Mapping and Monitoring of Mangroves towards Sustainable Management of West Coast, India}, BOOKTITLE = {Journal of Geophysics and Remote Sensing}. YEAR = {2014}}
Mangroves are one of the productive and highly adaptive ecosystems on the Earth, and provide invaluable services to the coastal communities. Ecological and sustainable management of the mangrove ecosystem requires crucial knowledge of variability and dynamics over a time and space. There are no reliable recent spatial extent estimates of mangroves or trend of coverage in West coast. The mapping from medium spatial resolution remote sensing data often leads to the underestimation of spatial extent. An attempt has been made to provide detailed information of mangroves species distribution using remote sensing data of high spatial resolution integrated with the other collateral field information through GIS. The coastline changes and the mangrove dynamics during 1989–2010 were assessed using supervised classifier technique provided the spatial distribution of mangrove species namely Rhizophora mucronata, Sonneratia caseolaris, Avicennia officinalis, Sonneratia alba, and Kandelia candel with classification good accuracy. Delineation of mangroves at species wise records based on extensive field data will be invaluable for appropriate management (eg plantation, eco-tourism) and conservation measures for estuaries of Central Western Ghats. Mangroves play pivotal role in providing vital ecosystem goods and services, but are under threat due to anthropogenic activities, affecting habitat for specialised fauna and food resources for humans, birds and fish. This necessitates interventions of regulatory authorities to partner with the local communities in the restoration and conservation of mangrove habitats.
Is current forest landscape research approaches providing the right insights? Observations from India context
Ramachandra Prasad Pillutla,Rajan Krishnan Sundara
Ecological Questions, EQ, 2014
@inproceedings{bib_Is_c_2014, AUTHOR = {Ramachandra Prasad Pillutla, Rajan Krishnan Sundara}, TITLE = {Is current forest landscape research approaches providing the right insights? Observations from India context}, BOOKTITLE = {Ecological Questions}. YEAR = {2014}}
One of the major challenges in the current scenario for ecological conservation is to quantify the forest landscape in its spatio-temporal domain and understand further implications of those. While the detailed study of the forest ecosystems may provide insights into biodiversity, carrying capacity and productive nature, most of the studies are restricted to single time/event inventory and focused on assessment of tree diversity patterns. Through the adoption of geospatial technologies like remote sensing and Geographical Information System (GIS), though forest monitoring has been possible, the linkages to the biodiversity distribution and its patterns are still at an empirical level, thus supporting broad measures of protection and preservation without accounting for the local/regional variability.
Sensor Simulation based Hyperspectral Image Enhancement with Minimal Spectral Distortion
Ankush Khandelwal,Rajan Krishnan Sundara
ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, ISPRS-APRSS, 2014
@inproceedings{bib_Sens_2014, AUTHOR = {Ankush Khandelwal, Rajan Krishnan Sundara}, TITLE = {Sensor Simulation based Hyperspectral Image Enhancement with Minimal Spectral Distortion}, BOOKTITLE = {ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences}. YEAR = {2014}}
In the recent past, remotely sensed data with high spectral resolution has been made available and has been explored for various agricultural and geological applications. While these spectral signatures of the objects of interest provide important clues, the relatively poor spatial resolution of these hyperspectral images limits their utility and performance. In this context, hyperspectral image enhancement using multispectral data has been actively pursued to improve spatial resolution of such imageries and thus enhancing its use for classification and composition analysis in various applications. But, this also poses a challenge in terms of managing the trade-off between improved spatial detail and the distortion of spectral signatures in these fused outcomes. This paper proposes a strategy of using vector decomposition, as a model to transfer the spatial detail from relatively higher resolution data, in association with sensor simulation to generate a fused hyperspectral image while preserving the inter band spectral variability. The results of this approach demonstrates that the spectral separation between classes has been better captured and thus helped improve classification accuracies over mixed pixels of the original low resolution hyperspectral data. In addition, the quantitative analysis using a rank-correlation metric shows the appropriateness of the proposed method over the other known approaches with regard to preserving the spectral signatures.
Status and future transition of rapid urbanizing landscape in central Western Ghats - CA based approach
Bharath Setturu ,Rajan Krishnan Sundara,Ramachandra T V
ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, ISPRS-APRSS, 2014
@inproceedings{bib_Stat_2014, AUTHOR = {Bharath Setturu , Rajan Krishnan Sundara, Ramachandra T V}, TITLE = {Status and future transition of rapid urbanizing landscape in central Western Ghats - CA based approach}, BOOKTITLE = {ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences}. YEAR = {2014}}
The land use changes in forested landscape are highly complex and dynamic, affected by the natural, socio-economic, cultural, political and other factors. The remote sensing (RS) and geographical information system (GIS) techniques coupled with multi-criteria evaluation functions such as Markov-cellular automata (CA–Markov) model helps in analysing intensity, extent and future forecasting of human activities affecting the terrestrial biosphere. Karwar taluk of Central Western Ghats in Karnataka state, India has seen rapid transitions in its forest cover due to various anthropogenic activities, primarily driven by major industrial activities. A study based on Landsat and IRS derived data along with CA–Markov method has helped in characterizing the patterns and trends of land use changes over a period of 2004–2013, expected transitions was predicted for a set of scenarios through 2013-2022. The analysis reveals the loss of pristine forest cover from 75.51% to 67.36%(1973 to 2013) and increase in agriculture land as well as built-up area of 8.65%(2013), causing impact on local flora and fauna. The other factors driving these changes are the aggregated level of demand for land, local and regional effects of land use activities such as deforestation, improper practices in expansion of agriculture and infrastructure development, deteriorating natural resources availability. The spatio temporal models helped in visualizing on-going changes apart from prediction of likely changes. The CA-Markov based analysis provides us insights into the localized changes impacting these regions and can be useful in developing appropriate mitigation management …
Inventorying and mapping of Mangroves with IRS Resourcesat-II Data
Bharath Setturu,Prakash N Mesta,Rajan Krishnan Sundara,Subash Chandran,Ramachandra T.V
NRSC USER INTERACTION MEET, NRSE, 2013
@inproceedings{bib_Inve_2013, AUTHOR = {Bharath Setturu, Prakash N Mesta, Rajan Krishnan Sundara, Subash Chandran, Ramachandra T.V}, TITLE = {Inventorying and mapping of Mangroves with IRS Resourcesat-II Data}, BOOKTITLE = {NRSC USER INTERACTION MEET}. YEAR = {2013}}
Mangroves are high productive estuarine ecosystem, protect barrage and defend the impact of big storms. Numerous studies attempted to map the locations of mangroves and analyse changes using different sensors including optical sensors: LANDSAT, IRS, SPOT, and other from past four decades. The present work attempts to identify the mangroves at species level using high resolution remote sensing data with field based measurements. The lack of spatial quantitative mangroves maps for the west coast has led to overlooking of the importance of mangroves and also the implementation of restoration programmes. Mangroves planting in the small estuaries of the Uttara Kannada district of Karnataka State during the recent years have yielded desired results of better ecology and higher biological production. The RS data using 2010 IRS p6 L4 MX having resolution of 5 M is useful in mapping the distribution of adult mangroves. However for mapping juveniles and shrubby forms require higher spectral resolution data. Using open source GIS software (QGIS) and remote sensing software (GRASS) and IRS imageries mangrove areas and potential areas available for planting in three river estuaries in the Honnavar Forest Division of Uttara Kannada were mapped. Mangrove species are recommended for planting in suitable areas based on salinity regimes. The effective forest management in the region shows mangroves have expanded in several regions due to the conservation measures and supervision. The net gain in mangroves could be attributed to new plantations by the State Forest Department and effective protection; agriculture to mangroves; mudflat to mangroves. The overall accuracy of satellite imagery processing was 90.89% and the Kappa index was 0.89. The work demonstrates the potential of high resolution multi-temporal satellite remote sensing data with ground based measurements for effective mapping mangrove forests in coastal regions.
Spatio-Spectral Method for Estimating Classified Regions with High Confidence Using MODIS Data
ANUJ KATIYAL,Rajan Krishnan Sundara
IOP Conference Series: Earth and Environmental Science., EES, 2013
@inproceedings{bib_Spat_2013, AUTHOR = {ANUJ KATIYAL, Rajan Krishnan Sundara}, TITLE = {Spatio-Spectral Method for Estimating Classified Regions with High Confidence Using MODIS Data}, BOOKTITLE = {IOP Conference Series: Earth and Environmental Science.}. YEAR = {2013}}
In studies like change analysis, the availability of very high resolution (VHR)/high resolution (HR) imagery for a particular period and region is a challenge due to the sensor revisit times and high cost of acquisition. Therefore, most studies prefer lower resolution (LR) sensor imagery with frequent revisit times, in addition to their cost and computational advantages. Further, the classification techniques provide us a global estimate of the class accuracy, which limits its utility if the accuracy is low. In this work, we focus on the sub-classification problem of LR images and estimate regions of higher confidence than the global classification accuracy within its classified region. The spectrally classified data was mined into spatially clustered regions and further refined and processed using statistical measures to arrive at local high confidence regions (LHCRs), for every class. Rabi season MODIS data of January 2006 & 2007 …
Land Surface Temperature Responses to Land Use Land Cover Dynamics.
Bharath Setturu,Rajan Krishnan Sundara,TV Ramachandra
Geoinformatics and Geostatistics, G&G, 2013
@inproceedings{bib_Land_2013, AUTHOR = {Bharath Setturu, Rajan Krishnan Sundara, TV Ramachandra}, TITLE = {Land Surface Temperature Responses to Land Use Land Cover Dynamics.}, BOOKTITLE = {Geoinformatics and Geostatistics}. YEAR = {2013}}
Land use and land cover (LULC) changes induced by human or natural processes drive biogeochemistryof the Earth influencing the climate at global as well as regional scales. Drastic changes in the land cover with the decline in vegetation and water bodies due to anthropogenic activities enhances the heat emission from land surface and atmospheric temperatures Increased land surface temperature (LST) is mainly due to increase in concentrated human activities, paved land cover or barren lands. Due to complexity of landscapes the sampling was difficult to derive LST and environmental response relationships. Temporal data acquired through space borne remote sensors has bridged the gap of temporal data for the entire earth surface. The current study explores the relation between surface biophysical parameters to sub-pixel thermal variations. The thermal infrared bands of remote sensing data help to retrieve LST, which are supplemented byground based measurements. Analysis of LST with LULC indicates a negative correlation between vegetation indices and LST. The general trend in the ambient temperature of Uttara Kannada over the 31 year period was established. Itclarifies that there was a fundamental drift of temperature rise in recent years, especially during the last decade.
Spatial Analysis of Indian Railways
RAJESH CHATURVEDI,Kshitij Mishra,Ramachandra Prasad Pillutla,Rajan Krishnan Sundara
India Geospatial Forum, IGF, 2013
@inproceedings{bib_Spat_2013, AUTHOR = {RAJESH CHATURVEDI, Kshitij Mishra, Ramachandra Prasad Pillutla, Rajan Krishnan Sundara}, TITLE = {Spatial Analysis of Indian Railways}, BOOKTITLE = {India Geospatial Forum}. YEAR = {2013}}
: Railway is the largest undertaking in the country. A retrospective study of prevailing conditions is necessary to assimilate the progress of the sector and how far have the changes introduced in previous few years been successful. The paper examines how the proposed changes in the Indian railways have changed the face of it through rigorous spatial analysis in diversified fields. A balanced analysis of shortcomings as well as achievements has been tried to be achieved through this paper for few selected regions of India.Each year there has to be more emphasis on enhancing the services provided. The rail budgets play a vital role and are responsible for taking care of it. The numerous promises made in terms of introduction of trains, electrification of routes, gauge conversion, doubling and increasing frequency of trains are made every year, in order to keep track of fulfillment of those decisions the spatial analysis is conducted rigorously. Railway has also witnessed a number of disasters in past few years; the spatial analysis conducted brings out few major reasons of those. A section of study also covers the topographic conditions in the state of Jammu and Kashmir that have not allowed the progress of railways in these regions.
Extraction of Disease Occurrence Patterns Using MiSTIC: Salmonellosis in Florida
Vipul Raheja,Rajan Krishnan Sundara
Online Journal of Public Health Informatics, OJPHI, 2013
@inproceedings{bib_Extr_2013, AUTHOR = {Vipul Raheja, Rajan Krishnan Sundara}, TITLE = {Extraction of Disease Occurrence Patterns Using MiSTIC: Salmonellosis in Florida}, BOOKTITLE = {Online Journal of Public Health Informatics}. YEAR = {2013}}
In this work, Spatio-Temporal Data Mining of disease surveillance data is done, to describe the underlying patterns in disease occurrences across populations and to identify possible causes that could explain them; for better disease core prediction, detection and management. MiSTIC algorithm is used to determine spatial spread of disease core regions (scale of disease prevalence), and the frequency & regularity of occurrence of different locations in space as disease cores. The results show good correlation between the etiologic factors of Salmonellosis and the detected core locations, in addition to the significant observation of highly localized nature of disease prevalence.
Extracting Road Features from Aerial Videos of Small Unmanned Aerial Vehicles
Deepak Rajamohan,Rajan Krishnan Sundara
ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, ISPRS-APRSS, 2013
@inproceedings{bib_Extr_2013, AUTHOR = {Deepak Rajamohan, Rajan Krishnan Sundara}, TITLE = {Extracting Road Features from Aerial Videos of Small Unmanned Aerial Vehicles}, BOOKTITLE = {ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences}. YEAR = {2013}}
With major aerospace companies showing interest in certifying UAV systems for civilian airspace, their use in commercial remote sensing applications like traffic monitoring, map refinement, agricultural data collection, etc., are on the rise. But ambitious requirements like real-time geo-referencing of data, support for multiple sensor angle-of-views, smaller UAV size and cheaper investment cost have lead to challenges in platform stability, sensor noise reduction and increased onboard processing. Especially in small UAVs the geo-referencing of data collected is only as good as the quality of their localization sensors. This drives a need for developing methods that pickup spatial features from the captured video/image and aid in geo-referencing. This paper presents one such method to identify road segments and intersections based on traffic flow and compares well with the accuracy of manual observation. Two test video datasets, one each from moving and stationary platforms were used. The results obtained show a promising average percentage difference of 7.01% and 2.48% for the road segment extraction process using moving and stationary platform respectively. For the intersection identification process, the moving platform shows an accuracy of 75% where as the stationary platform data reaches an accuracy of 100%.
Cost effective mapping, monitoring and visualisation of spatial patterns of urbanisation using FOSS
Bharath Setturu,Bharath H Aithal ,Rajan Krishnan Sundara,TV Ramachandra
OPEN SOURCE GEOSPATIAL RESOURCES TO SPEARHEAD DEVELOPMENT AND GROWTH, OSGEO, 2012
@inproceedings{bib_Cost_2012, AUTHOR = {Bharath Setturu, Bharath H Aithal , Rajan Krishnan Sundara, TV Ramachandra}, TITLE = {Cost effective mapping, monitoring and visualisation of spatial patterns of urbanisation using FOSS}, BOOKTITLE = {OPEN SOURCE GEOSPATIAL RESOURCES TO SPEARHEAD DEVELOPMENT AND GROWTH}. YEAR = {2012}}
Rapid urbanization is resulting in the irreversible land use and land cover (LULC) changes, which is the prime driver of global warming and consequent climate changes. This communication investigates landscape dynamics assessed through spatio-temporal pattern analysis using free and open source softwares [FOSS, http://ces. iisc. ernet. in/foss]. Bangalore has been experiencing urbanization tide since globalisation era and is one of the fastest growing cities in India. Migration and policy decision of setting up major facilities and industries seem to be the reason for rapid urbanisation with impacts on the local ecology and natural resources. As the city centre has reached the threshold of urbanisation, intense sprawl has been noticed at outskirts. In order to understand the urbanisation and sprawl dynamics, spatio-temporal analysis has been done in the city administrative region with 10 km buffer. To understand the dynamics at local level with drivers, spatial metrics is computed for the study region, which is divided into eight zones (radial), 13 circles of 2 km incrementing radius. The results show that extensive land conversion to urban utility has occurred since 1992 within the city and from 2000 conversion started at periphery, especially by industrialisation activities. The land scape metrics exhibited remarkable spatial and temporal variations and urban sprawl. Overall changes in the land use from 1973 to 2012 shows the increase in the built-up (paved surface) area from 1.87% to 29.33% and decrease in vegetation cover from 62.38% to 33.68%. The landscape metrics depicts the city is more concentrated at center and fragmented towards its …
Tsunami and tropical island ecosystems: a meta-analysis of studies in Andaman and Nicobar Islands
Ramachandra Prasad Pillutla,P Mamtha Lakshmi,Rajan Krishnan Sundara,Vijaya Bhole ,C. B. S. Dutt
Biodiversity and Conservation, BC, 2012
@inproceedings{bib_Tsun_2012, AUTHOR = {Ramachandra Prasad Pillutla, P Mamtha Lakshmi, Rajan Krishnan Sundara, Vijaya Bhole , C. B. S. Dutt}, TITLE = {Tsunami and tropical island ecosystems: a meta-analysis of studies in Andaman and Nicobar Islands}, BOOKTITLE = {Biodiversity and Conservation}. YEAR = {2012}}
Tropical islands are special and sensitive ecosystems which are subjected to various disturbances imposed by human activities and natural disasters. A detailed study about the changing landscape scenarios of these fragile island systems induced by various driving factors could be used for setting up measurements in support of conservation and sustainable development projects. The current research is a meta-analysis of the studies carried out in Andaman and Nicobar islands which analyzed the impact of tsunami of 2004 using geospatial tools. Based on the analysis, it was observed that the Nicobar islands were more affected compared to the Andaman islands. The majority of the researchers used pre- and post-tsunami satellite imagery and adopted visual interpretation method to delineate the changed classes. The study infers uplift of land in Andaman (exposing) and subsidence in Nicobar islands …
Risk analysis based on spatio-temporal characterization: a case study of disease risk mapping
Vipul Raheja,Rajan Krishnan Sundara
ACM SIGSPATIAL International Workshop on Use of GIS in Public Health., SIGSPATIAL, 2012
@inproceedings{bib_Risk_2012, AUTHOR = {Vipul Raheja, Rajan Krishnan Sundara}, TITLE = {Risk analysis based on spatio-temporal characterization: a case study of disease risk mapping}, BOOKTITLE = {ACM SIGSPATIAL International Workshop on Use of GIS in Public Health.}. YEAR = {2012}}
One of the challenges in risk analysis has been that the determinants which are identified are based on a causality-driven approach drawn largely from the correlation studies of underlying factors. These approaches not only require numerous thematic information layers-spatial and non-spatial, that may potentially represent the factors of interest, but also tend to ignore the spatial and temporal variability of the outcome itself (say, disease incidence). On the other hand, owing to the advances in surveillance and monitoring systems resulting in enhanced availability of spatially explicit data over the last 25 years, there is a need to use these effectively at understanding or explaining the phenomenon itself. In this paper, we propose a method to leverage the observed event data-both spatial and temporal characterizations of disease occurrences, to generate a risk map that will provide valuable insights into its
Efficient top-k queries for orthogonal ranges
S RAHUL,Prosenjit Gupta,Ravi Janardan,Rajan Krishnan Sundara
International Workshop on Algorithms and Computation, WALCOM, 2011
@inproceedings{bib_Effi_2011, AUTHOR = {S RAHUL, Prosenjit Gupta, Ravi Janardan, Rajan Krishnan Sundara}, TITLE = {Efficient top-k queries for orthogonal ranges}, BOOKTITLE = {International Workshop on Algorithms and Computation}. YEAR = {2011}}
Advances in sensing and data gathering technologies have resulted in an explosion in the volume of data that is being generated, processed, and archived. In particular, this information overload calls for new methods for querying large spatial datasets, since users are often not interested in merely retrieving a list of all data items satisfying a query, but would, instead, like a more informative \summary" of the retrieved items. An example is the so-called top-k problem, where the goal is to retrieve from a set of n weighted points in IRd the k most signi cant points, ranked by their weights, that lie in an orthogonal query box in IRd (rather than get a list of all points lying in the query box). In this paper, ecient and output-sensitive solutions are presented for this problem in two settings. In the rst setting, the k points are reported in arbitrary order and the underlying set can be updated dynamically through insertions and deletions of points. In the second setting, the k points are reported in sorted order of their weights.
Range-Aggregate Queries Involving Geometric Aggregation Operations
S RAHUL,ANANDA SWARUP DAS,Rajan Krishnan Sundara,Srinathan Kannan
International Workshop on Algorithms and Computation, WALCOM, 2011
@inproceedings{bib_Rang_2011, AUTHOR = {S RAHUL, ANANDA SWARUP DAS, Rajan Krishnan Sundara, Srinathan Kannan}, TITLE = {Range-Aggregate Queries Involving Geometric Aggregation Operations}, BOOKTITLE = {International Workshop on Algorithms and Computation}. YEAR = {2011}}
In this paper we consider range-aggregate query problems wherein we wish to preprocess a set S of geometric objects such that given a query orthogonal range q, a certain aggregation function on the objects S0 = S \ q can be answered eciently. Range-aggregate version of point enclosure queries, 1-d segment intersection, 2-d orthogonal seg- ment intersection (with/without distance constraint) are revisited and we improve the existing results for these problems. We also provide semi- dynamic (insertions) solutions to some of these problems. This paper is the rst attempt to provide dynamic solutions to problems involving geometric aggregation operations.
Design of 2-level Hierarchical Ring Networks
Kishore Kshirsagar,KAZA KESAV RAM,Rajan Krishnan Sundara
International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT, 2011
@inproceedings{bib_Desi_2011, AUTHOR = {Kishore Kshirsagar, KAZA KESAV RAM, Rajan Krishnan Sundara}, TITLE = {Design of 2-level Hierarchical Ring Networks}, BOOKTITLE = {International Congress on Ultra Modern Telecommunications and Control Systems and Workshops}. YEAR = {2011}}
Hierarchical Ring Networks are widely used in communication systems, logistics and shared multiprocessors. It is hence imperative that efficient design of these structures has to be carried out, to reduce costs of their installation and help increase the ease of management. In this paper, we look at the problem of designing efficient 2- level Hierarchical Ring Networks, in the context of link costs optimization. 2-level HRNs only have two kinds of rings - local rings to connect disjoint sets of nodes and a global ring to interconnect all the local rings. We present an algorithm that is based on Simulated Annealing. We present results for scenarios containing upto 300 nodes. The results obtained from the algorithm are most encouraging.
Tsunami and tropical island ecosystems: a meta-analysis of studies in Andaman and Nicobar Islands
Ramachandra Prasad Pillutla,P. M. Lakshmi ,Rajan Krishnan Sundara,Vijaya Bhole,Dutt C. B. S.
Biodiversity and Conservation, BC, 2011
@inproceedings{bib_Tsun_2011, AUTHOR = {Ramachandra Prasad Pillutla, P. M. Lakshmi , Rajan Krishnan Sundara, Vijaya Bhole, Dutt C. B. S.}, TITLE = {Tsunami and tropical island ecosystems: a meta-analysis of studies in Andaman and Nicobar Islands}, BOOKTITLE = {Biodiversity and Conservation}. YEAR = {2011}}
t Tropical islands are special and sensitive ecosystems which are subjected to various disturbances imposed by human activities and natural disasters. A detailed study about the changing landscape scenarios of these fragile island systems induced by various driving factors could be used for setting up measurements in support of conservation and sustainable development projects. The current research is a meta-analysis of the studies carried out in Andaman and Nicobar islands which analyzed the impact of tsunami of 2004 using geospatial tools. Based on the analysis, it was observed that the Nicobar islands were more affected compared to the Andaman islands. The majority of the researchers used preand post-tsunami satellite imagery and adopted visual interpretation method to delineate the changed classes. The study infers uplift of land in Andaman (exposing) and subsidence in Nicobar islands (inundation) with severe damage to the coastal elements like mangroves, coral reefs, plantations and in few cases interior forest. The analysis showed there were no records of the damage for some small islands. Finally, it is concluded that utility of microwave satellite data for change analysis will prove better in regions like Andaman and Nicobar where it is difficult to get cloud free optical data because of the high monsoon periods in these islands. It is also suggested that future work utilizing suitable temporal satellite imagery should focus on the extent of recovery of vegetation and other coastal elements which suffer the impact of disaster.
One-Reporting Queries
S RAHUL,Rajan Krishnan Sundara
European Workshop on Computational Geometry, EuroCG, 2010
@inproceedings{bib_One-_2010, AUTHOR = {S RAHUL, Rajan Krishnan Sundara}, TITLE = {One-Reporting Queries}, BOOKTITLE = {European Workshop on Computational Geometry}. YEAR = {2010}}
We are given a set S of n points in a d-dimensional space. Given a query orthogonal box q=d i=1[ai; bi] or a semi-in nite query box q=d i=1[ai;1), a One- reporting query reports YES if S q6= , else reports NO. The data structures introduced in this paper do not have their query time exponentially dependent on the dimension size, d, if the points are assumed to be randomly generated. Hence we partially break the curse of dimensionality" with which almost all the data structures for range searching su fer. The model of computation assumed is word-RAM
Mining Spatial Co-occurrence of Drought Events from Climate Data of India
KOLLUKUDURU SRAVANTHI,Rajan Krishnan Sundara
International Conference on Data Mining Workshops, ICDM-W, 2010
@inproceedings{bib_Mini_2010, AUTHOR = {KOLLUKUDURU SRAVANTHI, Rajan Krishnan Sundara}, TITLE = {Mining Spatial Co-occurrence of Drought Events from Climate Data of India}, BOOKTITLE = {International Conference on Data Mining Workshops}. YEAR = {2010}}
Increasingly in the recent past, the focus on climate change is moving towards the understanding of the occurrence, both in magnitude and frequency, of extreme climatic events like droughts and floods. In this paper, an effort has been made towards identification of spatial co-occurrence of droughts across various subdivisions of India, based on the historical climatological data of the past century. The results show that similarities in the climate forcing factors have a role in spatio-temporal correlation of events. Also, from the results, it is seen that the well-known spatial autocorrelations measures, such as Moran Index, are not sufficient to explain such cooccurrences of events.
A conceptual framework to analyse the land-use/ land-cover changes and its impact on phytodiversity: a case study of North Andaman Islands, India
Ramachandra Prasad Pillutla,Rajan Krishnan Sundara,C. B. S. Dutt,Roy P. S..
Biodiversity and Conservation, BC, 2010
@inproceedings{bib_A_co_2010, AUTHOR = {Ramachandra Prasad Pillutla, Rajan Krishnan Sundara, C. B. S. Dutt, Roy P. S..}, TITLE = {A conceptual framework to analyse the land-use/ land-cover changes and its impact on phytodiversity: a case study of North Andaman Islands, India}, BOOKTITLE = {Biodiversity and Conservation}. YEAR = {2010}}
Phytodiversity is affected both by natural and anthropogenic factors and in Island ecosystems these impacts can devastate or reduce diversity, if the native vegetation is lost. In addition to rich species richness and diversity, Island systems are the sites of high endemism and any threat to these ecosystems will consequently lead to loss and extinction of species. To understand the dynamics including feedbacks of these changes in phytodiversity of North Andaman Islands, a conceptual framework is proposed which focuses on understanding the land-use and land-cover changes and its impact with phytodiversity. In considering land-use and land-cover changes this work highlights the direct and indirect drivers of changes—socio-economic, biophysical and climatic factors. Migration of population, their socio economic needs and government policies were identified as major driving forces threatening the phytodiversity of these Islands. Apart from human beings, natural disasters like tsunami and introduced herbivorous animals like elephants also contributed to forest destruction in these Islands. The integrated analysis based on such framework will provide insights for holistic resource management including ecological conservation.
Color based urban scene classification using high resolution satellite imagery
VINAY PANDIT,Sudhir Nagendra Gupta,Rajan Krishnan Sundara
International conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV, 2009
@inproceedings{bib_Colo_2009, AUTHOR = {VINAY PANDIT, Sudhir Nagendra Gupta, Rajan Krishnan Sundara}, TITLE = {Color based urban scene classification using high resolution satellite imagery}, BOOKTITLE = {International conference on Image Processing, Computer Vision, and Pattern Recognition}. YEAR = {2009}}
Automatic object extraction from urban areas using high resolution satellite images remains an open research area. High variability in object features like color, shape and scale makes the urban scene classification challenging. Though significant amount of work has been done on detecting buildings, roads and other objects individually, not much work is present for complete urban scene classification. This paper proposes an algorithm that makes use of color as an important cue along with geometry and shadow information for complete urban scene classification.
Data Structures for Range Aggregation by Categories
S RAHUL,Prosenjit Gupta,Rajan Krishnan Sundara
Annual Canadian Conference on Computational Geometry, CCCG, 2009
@inproceedings{bib_Data_2009, AUTHOR = {S RAHUL, Prosenjit Gupta, Rajan Krishnan Sundara}, TITLE = {Data Structures for Range Aggregation by Categories}, BOOKTITLE = {Annual Canadian Conference on Computational Geometry}. YEAR = {2009}}
We solve instances of a general class of problems de- ned as follows: Preprocess a set S of possibly weighted colored geometric objects (e.g. points/orthogonal seg- ments/rectangles) in Rd, d 1 such that given a query orthogonal range q, we can report eciently for each dis- tinct color c of the points in S \q, the tuple < c;F(c) > where F(c) is a function (e.g. weighted sum, bounding box etc.) of the objects of color c in q.
IS RAPID URBANIZATION LEADING TO LOSS OF WATER BODIES?
Ramachandra Prasad Pillutla,Rajan Krishnan Sundara,Vijaya Bhole ,C.B.S. Dutt
Journal of Spatial Science, JSSc, 2009
@inproceedings{bib_IS_R_2009, AUTHOR = {Ramachandra Prasad Pillutla, Rajan Krishnan Sundara, Vijaya Bhole , C.B.S. Dutt}, TITLE = {IS RAPID URBANIZATION LEADING TO LOSS OF WATER BODIES?}, BOOKTITLE = {Journal of Spatial Science}. YEAR = {2009}}
Water bodies, the aquatic systems on land, are one of the important store houses for variety of wildlife as well as nesting and breeding sites for different kind of avifauna. Currently these water bodies are under tremendous human pressure due to rapid urbanization. The lakes and reservoirs, across the country, are in varying degrees of environmental degradation due to various anthropogenic activities. In this context a study was carried out in and around the city, covering an area of about 7800 km2 to identify the impact of expanding urbanization process on water bodies of the Hyderabad city using 1989 and 2001 satellite data. The results of the study showed reduction in water bodies both in the area (19%) as well as in number. A negative change was observed in the status of water bodies towards NE and NW directions of the city. Most of the water bodies either were encroached by urban expansion or reduced in size due to eutrophication process. The expanding IT sector and developments in real estate, acted as major driving forces that converted agricultural lands into residential plots and there by exerting pressure on the nearby water bodies. The change study here provides us with vital clues towards understanding the change in water bodies’ vis-à-vis the urban needs, economic growth, and its location characteristics within a rapidly urbanization region.
Assessment of tsunami and anthropogenic impacts on the forest of the North Andaman Islands, India
Ramachandra Prasad Pillutla, SUDHAKAR REDDY.c,Rajan Krishnan Sundara,K.S., Raza,Dutt, C.B.S
International Journal of Remote Sensing, IJRS, 2009
@inproceedings{bib_Asse_2009, AUTHOR = {Ramachandra Prasad Pillutla, SUDHAKAR REDDY.c, Rajan Krishnan Sundara, K.S., Raza, Dutt, C.B.S}, TITLE = {Assessment of tsunami and anthropogenic impacts on the forest of the North Andaman Islands, India}, BOOKTITLE = {International Journal of Remote Sensing}. YEAR = {2009}}
Forests are depleted drastically at higher rates to cope up the needs of increasing population. The present study was carried out to assess the impact of anthropogenic and natural disturbances (tsunami) on the vegetation of North Andaman islands using three different satellite images of different time period by visual image interpretation technique. A higher proportion of land cover and vegetation converted into agriculture, settlement, sand and water. Assessment of overall forest change from 1976 to 2005 is 117 Km 2 , with forest loss at the rate of 3.8 Km 2 / year. Simulation study for future forest scenario predicted an increase in agriculture / settlement area to be 196, 296, 392 and 492 Km 2 for the next 25, 50, 75 and 100 years, coupled with the conversion of forest areas of 131, 227, 320 and 427 Km 2 . Finally by the end of 100 years the estimated forest area of 1271 Km 2 (1999 data) reduces to 846 Km 2 if proper conservative actions are not taken.
Assessing forest canopy closure in a geospatial medium to address management concerns for tropical islands—Southeast Asia
Ramachandra Prasad Pillutla,Nidhi Nagabhatla,C. S. Reddy,Stutee Gupta,Rajan Krishnan Sundara,S. H. Raza,C.B.S .Dutt
Environmental Monitoring and Assessment, EMA, 2008
@inproceedings{bib_Asse_2008, AUTHOR = {Ramachandra Prasad Pillutla, Nidhi Nagabhatla, C. S. Reddy, Stutee Gupta, Rajan Krishnan Sundara, S. H. Raza, C.B.S .Dutt}, TITLE = {Assessing forest canopy closure in a geospatial medium to address management concerns for tropical islands—Southeast Asia}, BOOKTITLE = {Environmental Monitoring and Assessment}. YEAR = {2008}}
The present study outlines an approach to classify forest density and to estimate canopy closure of the forest of the Andaman and Nicobar archipelago. The vector layers generated for the study area using satellite data was validated with the field knowledge of the surveyed ground control points. The methodology adopted in this present analysis is three-tiered. First, the density stratification into five zones using visual
Suitability Mapping For Locating Special Economic Zone
Sudhir Nagendra Gupta,VINAY PANDIT,Rajan Krishnan Sundara
Remote Sensing and Photogrammetry Society Conference, RSPSC, 2008
@inproceedings{bib_Suit_2008, AUTHOR = {Sudhir Nagendra Gupta, VINAY PANDIT, Rajan Krishnan Sundara}, TITLE = {Suitability Mapping For Locating Special Economic Zone}, BOOKTITLE = {Remote Sensing and Photogrammetry Society Conference}. YEAR = {2008}}
A Special Economic Zone (SEZ) is a geographical region that has economic laws more liberal than a country's typical economic laws. In the recent years, India has witnessed a confrontation between farmers and the state, the reason being use of highly productive land for setting up SEZs with some of the cases occupying a centre stage in the World fora (Nandigram violence). In this paper, we propose an integrated solution for identifying nonagricultural lands for setting up SEZs using MODIS NDVI product and remote sensing techniques. The study also identifies multi- cropping practices and reinforces the potential of time-series MODIS data for mapping and land cover classification at regional scales.
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