Flood Forecasting with Sentinel-2 Images Using Machine Learning

Verfasst von

Viktoriia Hnatushenko, Vita Y. Kashtan, Volodymyr V. Hnatushenko, Christian Heipke

Abstract

This paper proposes a methodology for detecting flooded areas using Sentinel-2 images, followed by flood forecasting based on a combination of the deep neural network U-Net and a support vector machine (SVM). The U-Net architecture classifies a given Sentinel image into the two classes “water” and “no water”, the SVM subsequently performs a near-future prediction of flooded areas based on the U-Net results and additional information (DEM, land use information, precipitation data etc.). Experimental results demonstrate that for a test site in Ukraine the U-Net/SVM model achieves the highest overall accuracy (98.8%), slightly outperforming other models, including Random Forest and SVM. The resulting flood maps provide valuable information for planning rescue operations and territory management, allowing for rapidly identifying areas of flooding. It can thus contribute to a significant reduction in economic losses and an increase in emergency preparedness.

Details

Organisationseinheit(en): Institut für Photogrammetrie und Geoinformation
Externe Organisation(en): Ukrainian State University of Science and Technologies
Dnipro Polytechnic
Typ: Aufsatz in Konferenzband
Seiten: 583-588
Anzahl der Seiten: 6
Publikationsdatum: 28.07.2025
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Information systems, Geografie, Planung und Entwicklung
Ziele für nachhaltige Entwicklung: SDG 15 - Lebensraum Land
Elektronische Version(en): https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-583-2025 (Zugang: Offen )