Supervised Machine Learning for Flood Extent Detection with Optical Satellite Data
Author:
Affiliation:
1. RSS-Hydro,Research & Education Dept,Dudelange,Luxembourg,L-3593
2. Osir.io, The Drivery,Berlin,Germany,12099
Funder
European Space Agency
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10281394/10281399/10282274.pdf?arnumber=10282274
Reference11 articles.
1. A framework for multi-sensor satellite data to evaluate crop production losses: the case study of 2022 Pakistan floods
2. A Hierarchical Split-Based Approach for Parametric Thresholding of SAR Images: Flood Inundation as a Test Case
3. Delineation of flood-prone areas using modified topographic index for a river basin
4. Microwave remote sensing of flood inundation
5. Governance of flood risk data: A comparative analysis of government and insurance geospatial data for identifying properties at risk of flood
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1. Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-Image System to Create VarioCNN for Glacier Surges;Remote Sensing;2024-05-23
2. Breakthroughs in satellite remote sensing of floods;Frontiers in Remote Sensing;2024-04-29
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