Accurate information on urban surface water is important for assessing the role it
plays in urban ecosystem services in the context of human survival and climate
change. The precise extraction of urban water bodies from images is of great
significance for urban planning and socioeconomic development.
Deep learning is that learning method that recreates the human mind. It will precisely
separate elevated level choices from low-level choices of the info picture .
We have proposed a completely unique deep-learning architecture to extract various
earth objects from medium resolution satellite imagery using deep learning
approaches. We have used the Convolutional Encoder – Decoder model on the
Sentinel -2 Satellite images and Experimental results show that the proposed method
achieved higher accuracy for water feature extraction with average overall accuracy