Convolutional neural networks have developed a lot over the past decade and are used in almost all fields of science. Neural networks are already used to a large extent for detecting flooded area and provide automated detection with reliable results. These are important for assessing damage and planning the reconstruction of the flood areas. In the study, I used two models of the convolutional neural network MobileNetV2 and two stages of EfficientNet. For learning models, I used Sentinel-2 satellite data. To separate the flooded and not flooded areas, I created my own collection of annotations. We have developed a program that we use to pre-process data and learn models. The models used were tested by changing the hyperparameters. I also performed a product-level change test and a selection of Sentinel-2 satellite channels. In the third part of the tests, I improved the results only by enriching the amount of data. After each test, I analyzed the data and obtained an optimized model as a result that can successfully detect the flooded area. In the selected GMS-GIS, I used the developed method and tested it on new data.
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