Automatic Detection of Flood Extent and Volume Estimation from Sentinel-2 Satellite Images using Deep Learning Techniques in India

Floods are unexpected natural disasters that can have a major impact on human life, soil along bank erosion, damage vital infrastructure, road closures, economy standard, and society of various affected regions. An initial step of proper assessment is necessary for flood damage along with accurate measurements to easily restore essential damage of infrastructure, relief, and mitigation as quickly as possible. Nowadays, the rapid development of remote sensing images using deep learning as a most positive tool for accurately estimating the extent of overall flood detection surfaces. The monitoring of flood detections from remote sensing images still extends a few issues due to mostly varying from different weather changes conditions, cloud coverage areas that can have a limit to use of level of visible remote sensing satellite collected data. Moreover, Remote Sensing Satellite based observations may not always be mapped to the distribution's flood point peak, also it is very essential for both the flood extent and flood volume estimation. To overcome this challenge, we have presented a new remote sensing technology that integrates with a high resolution multi-spectral satellite data/information by using an advanced Deep Learning to accurately analyze remote sensing based observations. In our experiment, we use the European Space Agency (ESA) launched Sentinel type-1, Sentinel type-2 data and Digital Elevation Model (DEM) to accurately measure flood monitoring results. In our study, we reviewed a real example of the flood situation that happened in 2019 in Kolhapur. In our results, we evaluated a flood volume estimation at 0.0010 km3 in Kolhapur district. Finally, the proposed methodology provides an effective way to accurately motoring floods using low-cost satellite data and deep learning approaches. This project has the potential to improve the more accurate flood detection and mapping which can prevent an exactly timely response and immediate recovery efforts for flood surrounding affected areas.