Extraction of River Networks from Satellite Images using Image Processing & Deep Learning Techniques

River networks are widely observed and scrutinized for various purposes, which incorporate determining the terrestrial positions of water bodies, examining the gauge levels of the river, predicting river flows, and conserving sustainable energy resources as a consequence of Global warming. Extraction of these River networks on digital imagery systems are executed by various segmentation and machine learning model integration. In this paper, distinct datasets are used from Kaggle and Google Earth Engine, Segmentation methods such as Image segmentation, gray scaling, enhancement, global thresholding, and Deep Learning UNet Architecture are integrated with contemplation of extracting river networks from satellite images which result in achieving 80.98 % dice score for the developed UNet Model. Hence, these developed techniques can further be used for river extraction from satellite images. And can be applied to various semantic segmentation detection datasets.