Sentinel-1 SAR Images and Deep Learning for Water Body Mapping

Floods occur throughout the world and are becoming increasingly frequent and dangerous. This is due to different factors, among which climate change and land use stand out. In Mexico, they occur every year in different areas. Tabasco is a periodically flooded region, causing losses and negative consequences for the rural, urban, livestock, agricultural, and service industries. Consequently, it is necessary to create strategies to intervene effectively in the affected areas. Different strategies and techniques have been developed to mitigate the damage caused by this phenomenon. Satellite programs provide a large amount of data on the Earth's surface and geospatial information processing tools useful for environmental and forest monitoring, climate change impacts, risk analysis, and natural disasters. This paper presents a strategy for the classification of flooded areas using satellite images obtained from synthetic aperture radar, as well as the U-Net neural network and ArcGIS platform. The study area is located in Los Rios, a region of Tabasco, Mexico. The results show that U-Net performs well despite the limited number of training samples. As the training data and epochs increase, its precision increases.

Automated summary

Floods are increasing in frequency and danger worldwide, with climate change and land use being major contributing factors. In Mexico, floods occur annually in various regions, causing significant losses and negative impacts on multiple industries. This paper presents a strategy using satellite imagery, the U-Net neural network, and ArcGIS platform to classify flooded areas in Tabasco, Mexico. Results demonstrate that U-Net performs well despite limited training samples, with increased precision as training data and epochs increase.