In this paper, the authors propose an algorithm for automatic near-real-time flood mapping of the Amur River basin from Sentinel-2 MSI data using a U-net convolutional neural network adapted to the task. As a training set, we used Sentinel-2 Level-2A data and vector maps of river floods, created manually by specialists from the Far-Eastern Center of State Research Center for Space Hydrometeorology “Planeta”. According to the training results, Precision was 94.91%, Recall - 90.76%, F1-measure - 92.79%. High accuracy estimates and fast operation speed make it possible to use the developed algorithm for automatic near-real-time flood mapping of the Amur River basin in complex monitoring problems.
We describe an algorithm based on a convolutional neural network that detects cloud formations and snow cover in satellite images using textures. Herein, multispectral satellite images, received from a multizone scanning instrument used for hydrometeorological support and installed on the Russian satellite Electro-L No. 2, are used as input data. The problem of snow and cloud classification in the absence of a spectral channel in the range of 1.4 to 1.8 μm, which is necessary for their accurate separation, is considered. The developed algorithm can produce cloud and snow cover masks for an area limited by the values of the solar zenith angle in the range of 0 deg to 80 deg for daytime. Algorithm accuracy was evaluated using machine learning metrics and comparing its results with ground truth masks segmented manually by an experienced interpreter. In addition, we compared the resulting masks with a similar cloud mask product from the European Organisation for the Exploitation of Meteorological Satellites based on the data of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument installed on the Meteosat-8 satellite. According to the results of this comparison, we conclude that the cloud masks produced by the proposed convolutional neural network-based algorithm have a lower probability of false detection than products based on the SEVIRI data. The proposed algorithm is fully automatic, and it works in any season of the year during the daytime.
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