Deep Learning-Based Segmentation of Multi-Temporal Satellite Imagery for Flood Detection - Kashtan, Vita Yu.; Hnatushenko, Volodymyr V. (CEUR-WS Team, Aachen, Germany, 2025)
ENG: A deep learning-based pixel-based flood zone segmentation approach is proposed using multi-temporal satellite images and topographic and hydrological information. It is proposed to combine heterogeneous data (satellite images before and after the flood, digital elevation model, and hydrographic characteristics) into a single input tensor, allowing the neural network to consider the area's spatial and temporal dynamics and morphometric features. The architecture of the model ensures the preservation of the spatial detail of the flooded area through skip-connection mechanisms, which contributes to the correct identification of flood boundaries. Comparative analysis with FCNN, DeepLabv3, and BASNet confirmed the superiority of the proposed approach (F1-score 82%, Dice 82% for the category 'flooded areas'), which indicates its effectiveness for accurately detecting flooded areas.
V. Kashtan: ORCID 0000-0002-0395-5895; Vol. Hnatushenko: ORCID 0000-0003-3140-3788
Keywords: deep learning; pixel segmentation; flooding; multi-temporal satellite imagery; neural network; classification; КІТС; TECHNOLOGY::Information technology::Image analysis