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| 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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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.
Subject Keywords
Content
readme.md
Dataset Description
This dataset contains pixel-level samples for flood inundation mapping using Sentinel-1 SAR and Height Above the Nearest Drainage (HAND) data. The samples were derived from:
- Sen1Floods11 dataset: 446 manually labeled chips from global flood events.
- In-house U.S. dataset: 1,624 chips from 104 flood events across 20 US states.
Variables Included
Each file named as CaseName/ID_ChipCode.
Variables with Sen1Floods11 samples:
VV_db | Sentinel-1 VV polarization backscatter coefficient (in dB)
VH_db | Sentinel-1 VH polarization backscatter coefficient (in dB)
HAND | Height Above the Nearest Drainage (m)
label | Binary flood status (0 = non-flooded, 1 = flooded)
country | Unique identifier of the source flood event
clip_id | Unique identifier of the source image chip
Variables with In-house samples:
VV_db | Sentinel-1 VV polarization backscatter coefficient (in dB)
VH_db | Sentinel-1 VH polarization backscatter coefficient (in dB)
HAND | Height Above the Nearest Drainage (m)
label | Binary flood status (0 = non-flooded, 1 = flooded)
case_id | Unique identifier of the source flood event
clip_id | Unique identifier of the source image chip
Intended Use
The dataset is designed for:
- Training and evaluating machine learning or deep learning models for remote sensing flood inundation mapping.
- Testing model generalization across different regions, land covers, and climate zones.
Acknowledgement
Part of this dataset is derived from the Sen1Flood11 dataset (Bonafilia et al., 2020). Please cite the original publication if you use or build upon this dataset.
Reference:
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
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 |
|---|---|---|
| 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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