A Deep Neural Network Method for Water Areas Extraction Using Remote Sensing Data

Abstract

Water bodies on the Earth’s surface are an important part of the hydrological cycle. The water resources of the Kerch Peninsula at this moment can be described as a network with temporary streams and small rivers that dry up in summer. Partially, they are often used in fisheries. But since permanent field monitoring is quite financially and resource-intensive, it becomes necessary to find a way for the automated remote monitoring of water bodies using remote sensing data. In this work, we used remote sensing data obtained using the Sentinel-2 satellite in the period from 2017 to 2022 during the days of field expeditions to map the water bodies of the Kerch Peninsula. As a training data set for surface water prediction, field expeditions data were used. The area for test data collection is located near Lake Tobechikskoye, where there are five water bodies. The Keras framework, written in Python, was used to build the architecture of a deep neural network. The architecture of the neural network consisted of one flattened and four dense layers fully connected. As a result, it achieved a model prediction accuracy of 96% when solving the problem of extracting the area of the water surface using remote sensing data. The obtained model showed quite good results in the task of identifying water bodies using remote sensing data, which will make it possible to fully use this technology in the future both in hydrological studies and in the design and forecasting of fisheries.