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@inproceedings{bib_Mapp_2025, AUTHOR = {Malini, K. and Rajan, Krishnan Sundara and Nayak, Venkatesh }, TITLE = {Mapping Distribution of PM₁₀ Malady over Mosaic, Metro-Highlands: Validation of Multi-orbit Satellite Estimates against Mass-Scale Sensors to provide Hyperlocal Concentration Estimates.}, BOOKTITLE = {Journal of Geophysical Research: Atmospheres}. YEAR = {2025}}

Airborne PM₁₀ poses significant respiratory and cardiovascular risks to the vulnerable population. In high-elevation urban zones like Gachibowli, Hyderabad, understanding the spatiotemporal behaviour of PM₁₀ spread is essential to develop targeted health advisories. This is not achievable solely by increasing the number of monitoring stations. The local topography, Land Use/Land Cover (LULC) patterns and mixing conditions must be understood to identify pollution sources and dispersal dynamics. We estimated PM₁₀ concentrations based on Aerosol Optical Depth from two satellite sources - half-hourly, INSAT-3D geostationary satellite data at 11km; and daily polar-orbiting, MODIS Aqua data at 3km spatial resolution. By validating these PM₁₀ estimates against government-monitored Central Pollution Control Board (CPCB) sensors and low-cost academic AirIOT sensors of IIIT-Hyderabad, a near-continuous and hyperlocal PM₁₀ concentration estimate, near the ground surface, has been generated. Among the 4 Machine learning models evaluated, Multiple Linear Regression technique offered the best results when temperature, humidity and ERA-5 Planetary Boundary Layer Height were used as predictors. While satellite-based PM₁₀ estimate averages ranged from 61–72 µg/m³, AirIOT and CPCB ranged 24–57 µg/m³ and 99–106 µg/m³ respectively, during colder months. While AirIOT sensors are spread across varied microenvironments, the CPCB node lies near a main road with partial green cover. Satellite-derived values provide frequent, broad-scale estimates of pollutant distribution, and AirIOT and CPCB give point measurements. This resolution gap drives our effort to bridge scales and produce PM₁₀ estimates at ~1–2 km resolution with temporal continuity, while also capturing sub-grid variability and dissipation patterns. Thus, this integrated framework allows for hyperlocal pollution mapping for health-related early warning systems even in areas with sparse PM₁₀ sensor coverage. By correlating PM₁₀ subtypes with LULC and dispersion, health outcomes can be traced to specific exposure types. By extending this method across India, to megacities like New Delhi, a health risk classification can be developed along with guidelines for clinical response measures.

@inproceedings{bib_Spat_2025, AUTHOR = {Latwal, Avantika and Shaik, Rehna and Rajan, Krishnan Sundara }, TITLE = {Spatiotemporal analysis of Turbidity in Tropical Reservoirs using Sentinel-2 Satellite Imagery under Catchment Dynamics}, BOOKTITLE = {Remote Sensing Applications: Society and Environment}. YEAR = {2025}}

Contributing catchment land use patterns under rapid urbanization, agricultural practices and climatic variability in tropical regions significantly influence the sediment load of inland waterbodies such as reservoirs. This study presents a modeling framework to estimate qualitative turbidity dynamics in two tropical reservoirs (Bhadra and Tungabhadra) within same river basin but different landscape characteristics, using Sentinel-2 satellite data from 2016 to 2021 using Google Earth Engine (GEE). The framework demonstrates a robust capability of Sentinel-2 data, in detecting, mapping spatiotemporal trends and classifying turbidity levels from low to high along with impact of potential catchment characteristics. The Normalized Difference Turbidity Index (NDTI) values have a strong correlation with in-situ data (Earth Observing and Mapping EOMAP and field-based observations) having values > 10 NTU (nephelometric turbidity unit) with R2 = 0.81 and standard Error Estimate (SEE) of 5.3 respectively. In addition, the correlation between red band reflectance values with <10 NTU, has demonstrated a strong relation with an R2 and SEE of 0.74 and 1.7 respectively. Thus, the combination of NDTI and single red band reflectance values were used further for classifying the different turbidity levels (low, moderate and high). It was also observed that diverse land use patterns, particularly high proportions of urban and agriculture area along with rainfall, impact the annual and seasonal fluctuation of turbidity level in the reservoirs. The study demonstrates that remote sensing (RS) approach can effectively estimate turbidity levels in tropical reservoirs, highlighting the impact of catchment characteristics and supporting improved monitoring and sustainable water resource management.

@inproceedings{bib_Cont_2025, AUTHOR = {Pathakoti, Mahesh and Goru, Srinivasa Rao and Mandal, Swastika and Remala, Naga Anjani and Sitiraju, Srinivasa Rao and Chauhan, Prakash and Rajan, Krishnan Sundara and Asuri, Kanchana Lakshmi and Dangeti, Mahalakshmi Venkata and Thadishetty, Santhoshi and Peethani, Sujatha and Shaik, Ibrahim and Mamidala, Taran and Pushpanathan, Raja }, 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.

@inproceedings{bib_AUTO_2025, AUTHOR = {Gottipati, Srivathsa and Shreya, and Bhatnagar, Jai and Paruchuri, Praveen and Rajan, Krishnan Sundara }, 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.

@inproceedings{bib_Lear_2024, AUTHOR = {Jain, Aryamaan and Sharma, Avinash and Rajan, Krishnan Sundara }, 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.

@inproceedings{bib_Emis_2024, AUTHOR = {D.Venkata, Mahalakshmi and Chauhan, Prakash and Pathakoti, Mahesh and Kanchan, Lakshmi and Peethani, Sujatha and Shaik, Ibrahim and Rajan, Krishnan Sundara and Sagar, Vijay Kumar and Raja, Pushapanathan and K.Tiwari, Yogesh }, 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

@inproceedings{bib_Neig_2024, AUTHOR = {Rajan, Krishnan Sundara }, 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.

@inproceedings{bib_City_2024, AUTHOR = {Gujar, Shreyash Narendra and Pamireddy, Hitesh and Maradani, Naga Saiteja and Spanddhana, Sara and Chaudhari, Sachin and Rajan, Krishnan Sundara }, 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.

@inproceedings{bib_Spat_2024, AUTHOR = {Gujar, Shreyash Narendra and Spanddhana, Sara and Parmar, Ayu and Chaudhari, Sachin and Rajan, Krishnan Sundara }, 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.

@inproceedings{bib_Deve_2024, AUTHOR = {Parmar, Ayu and Spanddhana, Sara and Dwivedi, Ayush Kumar and Reddy, Chinthalapani Rajashekar and Patwardhan, Ishan and Dinesh, Bijjam Sai and Chaudhari, Sachin and Rajan, Krishnan Sundara and VEMURI, KAVITA }, 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.

@inproceedings{bib_Exam_2024, AUTHOR = {Latwal, Avantika and Kondraju, Tarun and Shaik, Rehana and Rajan, Krishnan Sundara }, 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.

@inproceedings{bib_Temp_2024, AUTHOR = {Pathakoti, Mahesh and D.V, Mahalakshmi and Kanchana, and Rajan, Krishnan Sundara and Bothale, Rajashree Vinod and Chauhan, Prakash and Taori, Alok }, 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.

@inproceedings{bib_Wate_2023, AUTHOR = {Latwal, Avantika and Rajan, Krishnan Sundara and Shaik, Rehana }, 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.

@inproceedings{bib_Dete_2023, AUTHOR = {Latwal, Avantika and Shaik, Rehana and Rajan, Krishnan Sundara }, 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.

@inproceedings{bib_MODE_2023, AUTHOR = {Rajan, Krishnan Sundara and 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}}

@inproceedings{bib_Deca_2023, AUTHOR = {Reddy, Eravelli Ankitha and Rajan, Krishnan Sundara }, 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.

@inproceedings{bib_SEAS_2023, AUTHOR = {Reddy, Eravelli Ankitha and Rajan, Krishnan Sundara }, 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

@inproceedings{bib_MODE_2023, AUTHOR = {Shreya, and Rajan, Krishnan Sundara }, 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.

@inproceedings{bib_Mode_2023, AUTHOR = {Shreya, and Rajan, Krishnan Sundara }, 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.

@inproceedings{bib_Unde_2023, AUTHOR = {TEJA, K TARUN and Rajan, Krishnan Sundara }, 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

@inproceedings{bib_Spat_2023, AUTHOR = {Reddy, Eravelli Ankitha and Rajan, Krishnan Sundara }, 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.

@inproceedings{bib_Impl_2023, AUTHOR = {Pathakoti, Mahesh and Kanchana, A. L. and Mahalakshmi, D. V. and Guha, Tania and Raja, P. and , K. Nalini and Rajan, Krishnan Sundara and Sai, M. V. R. Sesha and Dadhwal, V. K. }, 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.

@inproceedings{bib_Thre_2023, AUTHOR = {Pathakoti, Mahesh and D.V, Mahalakshmi and Gaddamidi), Sreenivas and Arun, S.S. and Bothale, R.V. and Chauhan, P. and Chauhan, Prakash and Rajan, Krishnan Sundara and Chandra, Naveen }, 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.

@inproceedings{bib_Does_2023, AUTHOR = {Archana, Pragada and Rajan, Krishnan Sundara }, 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

@inproceedings{bib_Grou_2022, AUTHOR = {Sagar, Vijay Kumar and Pathakoti, Mahesh and D.V, Mahalakshmi and Rajan, Krishnan Sundara and M.V.R, Sesha Sai and Hase, Frank and Dubravica, Darko and Sha, Mahesh Kumar }, 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.

@inproceedings{bib_Adap_2022, AUTHOR = {Jain, Aryamaan and Sharma, Avinash and Rajan, Krishnan Sundara }, 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.

@inproceedings{bib_Gene_2022, AUTHOR = {Tejaswini, Venkumahanti and Rao, Pyla Kesava and Rao, E. N. Dhanamjaya and Ravoori, Nagaraja and Sinha, S. K. and Rajan, Krishnan Sundara }, 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.

@inproceedings{bib_A_BI_2022, AUTHOR = {Tejaswini, V and Rao, P. Kesava and Rao, E. N. Dhanamjaya and Ravoori, Nagaraja and Sinha, S. K. and Rajan, Krishnan Sundara }, 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.

@inproceedings{bib_Seis_2022, AUTHOR = {Srivastav, Ambika and Stayam, Neelima and Rajan, Krishnan Sundara }, 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.

@inproceedings{bib_SPAT_2022, AUTHOR = {Deb, Souradeep and Dwivedi, Ayush Kumar and Chaudhari, Sachin and Rajan, Krishnan Sundara }, 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

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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

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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

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Geospatial object detection using machine learning-aviation case study

Durga Prasad Dhulipudi,Krishnan Sundara Rajan

Integrated Communications Navigation and Surveillance Conference, ICNS, 2021

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Generation of Spatial Profiles & Mapping of Volcanic Ash Distribution

MALINI K,Krishnan Sundara Rajan

International Symposium on Digital Earth, ISDE, 2021

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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

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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

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GENERATING SPATIAL DISTRIBUTION OF VOLCANIC ASH SPREAD

MALINI K,Rajan Krishnan Sundara

International Geoscience and Remote Sensing Symposium, IGARSS, 2021

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Geospatial Object Detection using Machine Learning-Aviation Case Study

Durga Prasad Dhulipudi,Rajan Krishnan Sundara

Integrated Communications Navigation and Surveillance Conference , ICNS, 2021

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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

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DAV - DATA ANALYTICS AND VISUALIZATION SYSTEM FOR ROADS

Bhavana Gannu,Rajan Krishnan Sundara

INTERNATIONAL SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING, ISPRS, 2020

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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MAARG: GEO-PROCESSING AND INFORMATION FUSION APPROACH FOR ROADmCONDITION AND SURFACE MONITORING

GANNU BHAVANA,Rajan Krishnan Sundara

Asian Conference on Remote Sensing, ACRS, 2018

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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

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MODELLING SHIFTING CULTIVATION AND CHANGES TO LAND USE IN BARAK VALLEY

JYOTI MISRA,Rajan Krishnan Sundara

Asian Conference on Remote Sensing, ACRS, 2018

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IMPROVING PATHQUERY PERFORMANCE IN PGROUTING USING AMAPGENERALIZATIONAPPROACH

Sankepally Rohith Reddy,Rajan Krishnan Sundara

INTERNATIONAL SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING, ISPRS, 2018

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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

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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

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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

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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

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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

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OBJECT BASED FUSION OF MULTI-SENSOR IMAGERY WHILE PRESERVINGSPECTRALLY SIGNIFICANT INFORMATION

MAYANK GOYAL,Rajan Krishnan Sundara

International Geoscience and Remote Sensing Symposium, IGARSS, 2017

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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

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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

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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

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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

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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

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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

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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

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QUERY SUPPORT FOR GMZ.

AYUSH KHANDELWAL,Rajan Krishnan Sundara

INTERNATIONAL SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING, ISPRS, 2017

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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

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GMZ: A GML COMPRESSION MODEL FOR WEBGIS

AYUSH KHANDELWAL,Rajan Krishnan Sundara

ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, ISPRS-APRSS, 2017

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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FAST ICA BASED ALGORITHM FOR BUILDING DETECTION FROM VHR IMAGERY

LIPIKA AGARWAL,Rajan Krishnan Sundara

International Geoscience and Remote Sensing Symposium, IGARSS, 2015

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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

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Is current forest landscape research approaches providing the right insights? Observations from India context

Ramachandra Prasad Pillutla,Rajan Krishnan Sundara

Ecological Questions, EQ, 2014

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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

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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

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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

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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

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Land Surface Temperature Responses to Land Use Land Cover Dynamics.

Bharath Setturu,Rajan Krishnan Sundara,TV Ramachandra

Geoinformatics and Geostatistics, G&G, 2013

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Spatial Analysis of Indian Railways

RAJESH CHATURVEDI,Kshitij Mishra,Ramachandra Prasad Pillutla,Rajan Krishnan Sundara

India Geospatial Forum, IGF, 2013

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Extraction of Disease Occurrence Patterns Using MiSTIC: Salmonellosis in Florida

Vipul Raheja,Rajan Krishnan Sundara

Online Journal of Public Health Informatics, OJPHI, 2013

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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

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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

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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

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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

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Efficient top-k queries for orthogonal ranges

S RAHUL,Prosenjit Gupta,Ravi Janardan,Rajan Krishnan Sundara

International Workshop on Algorithms and Computation, WALCOM, 2011

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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

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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

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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

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One-Reporting Queries

S RAHUL,Rajan Krishnan Sundara

European Workshop on Computational Geometry, EuroCG, 2010

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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

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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

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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

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Data Structures for Range Aggregation by Categories

S RAHUL,Prosenjit Gupta,Rajan Krishnan Sundara

Annual Canadian Conference on Computational Geometry, CCCG, 2009

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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

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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

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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

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Suitability Mapping For Locating Special Economic Zone

Sudhir Nagendra Gupta,VINAY PANDIT,Rajan Krishnan Sundara

Remote Sensing and Photogrammetry Society Conference, RSPSC, 2008

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