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JDR Vol.21 No.1 pp. 201-211
(2026)
doi: 10.20965/jdr.2026.p0201

Paper:

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

Ryosuke Nagato*, Ira Karrel San Jose* ORCID Icon, Sesa Wiguna** ORCID Icon, Ryohei Kametaka* ORCID Icon, Bruno Adriano** ORCID Icon, Erick Mas** ORCID Icon, and Shunichi Koshimura**,† ORCID Icon

*Department of Civil and Environmental Engineering, Tohoku University
6-6-06 Aramaki Aza-Aoba, Aoba-ku, Sendai, Miyagi 980-8579, Japan

**International Research Institute of Disaster Science, Tohoku University
Sendai, Japan

†Corresponding author

Received:
August 30, 2025
Accepted:
November 20, 2025
Published:
February 1, 2026
Creative Commons License
Keywords:
ALOS-2 PALSAR-2 data, flood extent mapping, urban flood, deep learning, multi-modality
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.

Flood mapping using SAR and geographical features

Flood mapping using SAR and geographical features

Cite this article as:
R. Nagato, I. Jose, S. Wiguna, R. Kametaka, B. Adriano, E. Mas, and S. Koshimura, “Comparative Analysis of PALSAR-2 and Geographical Features for Mapping Urban and Non-Urban Flooded Areas,” J. Disaster Res., Vol.21 No.1, pp. 201-211, 2026.
Data files:
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Last updated on Feb. 04, 2026