Flooded Areas Detection through SAR Images and U-NET Deep Learning Model

Fernando Pech-May, Julio Víctor Sanchez-Hernández, Luis Antonio López-Gómez, Jorge Magaña-Govea, Edna Mariel Mil-Chontal


Floods are common in much of the world, this is due to different factors among which climate change and land use stand out. In Mexico they happen every year in different entities. Tabasco is an entity that is periodically flooded, 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. Therefore, 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 as well as geospatial information processing tools that are useful for environmental and forest monitoring, climate change impacts, risk analysis, natural disasters, among others. This paper presents a strategy for the classification of flooded areas using satellite images radar of synthetic aperture and the U-NET neural network. The study area is centered on Los R´ıos, region of Tabasco, Mexico. The partial results show that U-NET performs well despite the limited amount in the training samples. As training data and epochs increased, its accuracy increased.


Deep learning and SAR, sentinel-1 SAR, flood detection

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