DC4Flood: A Deep Clustering Framework for Rapid Flood Detection Using Sentinel-1 SAR Imagery
Author:
Affiliation:
1. Section Hydrology, GFZ German Research Center for Geosciences, Potsdam, Brandenburg, Germany
2. German Aerospace Center (DLR), Remote Sensing Technology Institute, EO Data Science, Wessling, Bayern, Germany
Funder
Helmholtz AI Project (semi/un)Supervised Machine Learning Flood Damage Assessment
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/8859/10365397/10504782.pdf?arnumber=10504782
Reference23 articles.
1. Normalized Difference Flood Index for rapid flood mapping: Taking advantage of EO big data
2. Rapid mapping with remote sensing data during flooding 2005 in Switzerland by object-based methods: a case study
3. Trends in flood losses in Europe over the past 150 years
4. Rapid flood and damage mapping using synthetic aperture radar in response to Typhoon Hagibis, Japan
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2. A satellite imagery-driven framework for rapid resource allocation in flood scenarios to enhance loss and damage fund effectiveness;Scientific Reports;2024-08-20
3. HistSegNet: Histogram Layered Segmentation Network for SAR Image-Based Flood Segmentation;IEEE Geoscience and Remote Sensing Letters;2024
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