Comparative Analysis of PALSAR-2 and Geographical Features for Mapping Urban and Non-Urban Flooded Areas

Abstract

Owing to recent extreme weather events, flood risk has been rising annually, increasing the demand for fast and accurate flood mapping. Synthetic aperture radar imagery has received considerable attention for flood-mapping applications owing to its all-weather, day-and-night imaging capabilities. Although previous studies have achieved accurate mapping in non-urban areas, challenges remain for urban regions. This study focuses on flood events in Japan by employing a deep learning model and PALSAR-2 imagery to classify non-flooded areas, floods in open areas, and floods in urban areas. To understand the complex spectral characteristics specific to urban areas, this study investigates the integration of geographical features, such as slope and building footprints, into the segmentation process. The experimental results suggest that the inclusion of these supplementary data improves the prediction performance of the trained models.