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| Type: | Resource | |
| Storage: | The size of this resource is 1.1 MB | |
| Created: | Sep 21, 2025 at 4:18 p.m. (UTC) | |
| Last updated: | Sep 24, 2025 at 1:42 p.m. (UTC) | |
| Published date: | Sep 24, 2025 at 1:42 p.m. (UTC) | |
| DOI: | 10.4211/hs.fb2fb1e511f7456c8379912db441845a | |
| Citation: | See how to cite this resource | |
| Content types: | CSV Content |
| Sharing Status: | Published |
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| Views: | 1683 |
| Downloads: | 68 |
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Abstract
This dataset contains pixel-level training samples used for developing and validating a deep learning (MLP) model for flood inundation mapping. Samples were derived from two sources: (1) 466 manually labeled image chips from the Sen1Floods11 dataset and (2) 1,624 image chips from an in-house dataset of 104 flood events across the continental United States (CONUS). Each sample represents one pixel, with four key variables: Sentinel-1 VV backscatter, Sentinel-1 VH backscatter, Height Above Nearest Drainage (HAND), and flood status label (0 = non-flooded, 1 = flooded), as well as several auxiliary variables: Country and Chip ID for Sen1Flood11 samples while Case ID and Clip ID for In-House samples.
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Related Resources
| The content of this resource is derived from | Bonafilia D, Tellman B, Anderson T, Issenberg E. Sen1Floods11: a georeferenced dataset to train and test deep learning flood algorithms for Sentinel-1. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE; 2020. doi:10.1109/cvprw50498.2020.00113 |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| NOAA Cooperative Institute Program | Cooperative Institute for Research to Operations in Hydrology (CIROH) | NA22NWS4320003 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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