Integration of Remote Sensing and Crowdsourced Data for Fine-Grained Urban Flood Detection

In the context of frequent global flood disasters, flood detection is of great significance for emergency management and human sustainable development, especially in urban areas with increasing population and socio-economic activities. However, there are similar reflection/scattering characteristics...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2024, Vol.17, p.13523-13532
Main Authors: Liu, Zhenjie, Li, Jun, Wang, Lizhe, Plaza, Antonio
Format: Article
Language:English
Subjects:
Accuracy
Data fusion
flood detection
Floods
Land surface
OpenStreetMap (OSM)
Remote sensing
Sentinel-1
supervised classification
Synthetic aperture radar
Urban areas
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Summary:In the context of frequent global flood disasters, flood detection is of great significance for emergency management and human sustainable development, especially in urban areas with increasing population and socio-economic activities. However, there are similar reflection/scattering characteristics between flooded and nonflooded land use and land cover (LULC) classes in complex urban environments, which limit the accurate detection of floods. In this study, we develop a new method for fine-grained and accurate flood detection by integrating multitemporal Sentinel-1 synthetic aperture radar images, OpenStreetMap data, and convolutional neural networks. We take the 2017 Houston flood event as a test case, where the study areas are divided into six fine-grained LULC classes, i.e., residential areas, service areas, main roads, forest, grassland, and waterways. Based on the information of fine-grained LULC classification, the proposed method performs more prominently than the baseline methods for urban flood detection. Specifically, compared with such baseline methods, F1 score, overall accuracy, and Kappa increase by more than 3.96%, 4.53%, and 9.26%, respectively. The integration of remote sensing and crowdsourced data provides a new perspective for flood detection in complex urban environments, thus supporting emergency management.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2024.3433010