Pub Date : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358928
Manish Singh, Manish Shekher, N. Jacob, Radhadevi, V. R. Venkataraman
Road extraction from high resolution satellite imagery has been a challenging task. The problem has been attempted by many people employing different methods and techniques and many have been able to solve it to a large extent. The novelty of this paper is to reach the end goal of providing a final product which can be used to generate semantically meaningful applications like vehicle detection, vehicle counting and determining the size of vehicle on the road. In this paper, an approach of road delineation in high resolution multi-spectral satellite imagery is proposed using Deep Neural Networks to generate a road binary mask. The binary mask comprising of objects is further processed with image processing techniques. Whereas to reduce the non-road objects, which are classified as road, object attributes such as object size and shape are used. The refined objects are converted into a shape file of road. Various challenges faced along the way and some useful observations and algorithmic strategies to achieve the end goal have been discussed in this paper.
{"title":"Automatic Road Delineation Using Deep Neural Network","authors":"Manish Singh, Manish Shekher, N. Jacob, Radhadevi, V. R. Venkataraman","doi":"10.1109/InGARSS48198.2020.9358928","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358928","url":null,"abstract":"Road extraction from high resolution satellite imagery has been a challenging task. The problem has been attempted by many people employing different methods and techniques and many have been able to solve it to a large extent. The novelty of this paper is to reach the end goal of providing a final product which can be used to generate semantically meaningful applications like vehicle detection, vehicle counting and determining the size of vehicle on the road. In this paper, an approach of road delineation in high resolution multi-spectral satellite imagery is proposed using Deep Neural Networks to generate a road binary mask. The binary mask comprising of objects is further processed with image processing techniques. Whereas to reduce the non-road objects, which are classified as road, object attributes such as object size and shape are used. The refined objects are converted into a shape file of road. Various challenges faced along the way and some useful observations and algorithmic strategies to achieve the end goal have been discussed in this paper.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"2 1","pages":"94-97"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73022986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358934
Mohammadsajid Khalifa, Arpita Pacheril, Naik Aaieda, Tejas Turakhia, Rajesh C. Iyer, A. Chhabra, M. Pandya
In order to understand the effect of pollution on the atmosphere over Ahmedabad city we have extracted two years (2017-18) of satellite and ground data. RF over Ahmedabad is determined by this data. For the satellite data, we used two different Radiative Transfer Model’s (COART & SBDART) while for ground measurement we only used SBDART to estimate RF. COART results for MODIS indicates 6.24% increase RFATM in winter 2018 compared to 2017 that is very similar to SBDART results for MODIS. Ground measurements with SBDART indicates 46.89% increase in RFATM in winter 2018 compared to winter 2017. AOD observations from MODIS are smaller than the ground-based ones and that particularly the change from 2017 to 2018 is much smaller in MODIS than observed at the ground. The study presents comparison of RF using two different model approaches for winter 2017 & 2018. However, the study concludes that anthropogenic activities are resulting to RF.
{"title":"Variation of Radiative Forcing Over Ahmedabad City","authors":"Mohammadsajid Khalifa, Arpita Pacheril, Naik Aaieda, Tejas Turakhia, Rajesh C. Iyer, A. Chhabra, M. Pandya","doi":"10.1109/InGARSS48198.2020.9358934","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358934","url":null,"abstract":"In order to understand the effect of pollution on the atmosphere over Ahmedabad city we have extracted two years (2017-18) of satellite and ground data. RF over Ahmedabad is determined by this data. For the satellite data, we used two different Radiative Transfer Model’s (COART & SBDART) while for ground measurement we only used SBDART to estimate RF. COART results for MODIS indicates 6.24% increase RFATM in winter 2018 compared to 2017 that is very similar to SBDART results for MODIS. Ground measurements with SBDART indicates 46.89% increase in RFATM in winter 2018 compared to winter 2017. AOD observations from MODIS are smaller than the ground-based ones and that particularly the change from 2017 to 2018 is much smaller in MODIS than observed at the ground. The study presents comparison of RF using two different model approaches for winter 2017 & 2018. However, the study concludes that anthropogenic activities are resulting to RF.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"1 1","pages":"173-176"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75371170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358939
Komal Daxini, Tejas Turakhia, Rajesh C. Iyer, A. Chhabra
This present study analyzes the air quality of college campus, located in Ahmedabad, using the measurements of Particulate Matter (PM), Black Carbon (BC) and Aerosol Optical Depth (AOD) and, for the year 2017 & 2018 when the college was not functional. The PM data analysis showed clear diurnal cycles at the study site. Due to poor dispersion conditions and suspension of fine particles in the ambient air for longer hours, the values obtained for PM10 is 173.02 μg/m3 and for PM2.5 is 75.74 μg/m3 which is almost double than the permissible limits by National Ambient Air Quality Standards (NAAQS). The BC concentration shows diurnal variation with concentration 9.97μg/m3 and 24.34μg/m3 during morning and night respectively. The AOD angstrom characteristic shows the dominance of coarser particles with hazy condition. This analysis pointed that the air quality of our campus is better compared to city, but still it is not within the permissible limits.
{"title":"Assessment of Ambient Air Quality of a College Campus","authors":"Komal Daxini, Tejas Turakhia, Rajesh C. Iyer, A. Chhabra","doi":"10.1109/InGARSS48198.2020.9358939","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358939","url":null,"abstract":"This present study analyzes the air quality of college campus, located in Ahmedabad, using the measurements of Particulate Matter (PM), Black Carbon (BC) and Aerosol Optical Depth (AOD) and, for the year 2017 & 2018 when the college was not functional. The PM data analysis showed clear diurnal cycles at the study site. Due to poor dispersion conditions and suspension of fine particles in the ambient air for longer hours, the values obtained for PM10 is 173.02 μg/m3 and for PM2.5 is 75.74 μg/m3 which is almost double than the permissible limits by National Ambient Air Quality Standards (NAAQS). The BC concentration shows diurnal variation with concentration 9.97μg/m3 and 24.34μg/m3 during morning and night respectively. The AOD angstrom characteristic shows the dominance of coarser particles with hazy condition. This analysis pointed that the air quality of our campus is better compared to city, but still it is not within the permissible limits.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"108 1","pages":"193-196"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87397429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-25DOI: 10.1109/InGARSS48198.2020.9358961
Javier Ruiz-Ramos, A. Berardi, A. Marino, Deepayan Bhowmik, Matthew G. Simpson
Wetlands are among the most productive natural ecosystems in the world, generally being important biodiversity hotspots. However, the complex nature of these landscapes together with the fragile and dynamic relationships among the organisms inhabiting these regions, make wetland ecosystems especially vulnerable to environmental disturbance, such as climate change. Thus, developing new automated systems which allow the continuous monitoring and mapping of wetland dynamics is crucial for informing decision-making and preserving their natural health. Synthetic Aperture Radar (SAR) sensors deployed on satellite platforms such as SENTINEL-1 are increasingly recognized as essential for wetland monitoring. The high sensitivity of SAR sensors to environmental variation makes them particularly suitable for investigating the hydrological processes occurring within these ecosystems.The main objective of this paper is to propose a rapid polarimetric SAR (PolSAR) change detection tool for monitoring and mapping the flood dynamics and environmental condition of the North Rupununi region, Guyana. By making use of dense Sentinel-1 timeseries data and the Google Earth Engine (GEE) platform, we were able to map temporal open water and temporal flooded vegetation areas in a continuous and near-real time basis. The outcomes derived from this study significantly contributed to identify the hydrological mechanisms of the region of study while providing essential and valuable information for rapid response and environmental impact assessment.
{"title":"Assessing Hydrological Dynamics of Guyana’s North Rupununi Wetlands Using Sentinel-1 Sar Imagery Change Detection Analysis on Google Earth Engine","authors":"Javier Ruiz-Ramos, A. Berardi, A. Marino, Deepayan Bhowmik, Matthew G. Simpson","doi":"10.1109/InGARSS48198.2020.9358961","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358961","url":null,"abstract":"Wetlands are among the most productive natural ecosystems in the world, generally being important biodiversity hotspots. However, the complex nature of these landscapes together with the fragile and dynamic relationships among the organisms inhabiting these regions, make wetland ecosystems especially vulnerable to environmental disturbance, such as climate change. Thus, developing new automated systems which allow the continuous monitoring and mapping of wetland dynamics is crucial for informing decision-making and preserving their natural health. Synthetic Aperture Radar (SAR) sensors deployed on satellite platforms such as SENTINEL-1 are increasingly recognized as essential for wetland monitoring. The high sensitivity of SAR sensors to environmental variation makes them particularly suitable for investigating the hydrological processes occurring within these ecosystems.The main objective of this paper is to propose a rapid polarimetric SAR (PolSAR) change detection tool for monitoring and mapping the flood dynamics and environmental condition of the North Rupununi region, Guyana. By making use of dense Sentinel-1 timeseries data and the Google Earth Engine (GEE) platform, we were able to map temporal open water and temporal flooded vegetation areas in a continuous and near-real time basis. The outcomes derived from this study significantly contributed to identify the hydrological mechanisms of the region of study while providing essential and valuable information for rapid response and environmental impact assessment.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"4 4 1","pages":"5-8"},"PeriodicalIF":0.0,"publicationDate":"2020-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82838069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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