SEN12-FLOOD : A SAR and Multispectral Dataset for Flood Detection
These last decades, Earth Observation brought quantities of new perspectives from geosciences to human activity monitoring. As more data became available, artificial intelligence techniques led to very successful results for understanding remote sensing data. Moreover, various acquisition techniques such as Synthetic Aperture Radar (SAR) can also be used for problems that could not be tackled only through optical images. This is the case for weather-related disasters such as floods or hurricanes, which are generally associated with large clouds cover. Yet, machine learning on SAR data is still considered challenging due to the lack of available labeled data. This dataset is composed of co-registered optical and SAR images time series for the detection of flood events.
Dataset ID | sen12floods |
DOI | 10.21227/w6xz-s898browse DOI |
Creator | CNAM |
Contact | Bertrand.Le.Saux@esa.int, clement.rambour@cnam.fr |
Documentation
Citation
Clément Rambour, Nicolas Audebert, Elise Koeniguer, Bertrand Le Saux, Michel Crucianu, Mihai Datcu, September 14, 2020, "SEN12-FLOOD : a SAR and Multispectral Dataset for Flood Detection ", IEEE Dataport, doi: https://dx.doi.org/10.21227/w6xz-s898.
Python Client example
from radiant_mlhub import Dataset
ds = Dataset.fetch('sen12floods')
for c in ds.collections:
print(c.id)
Download Dataset
Source Imagery Collections
Description | SEN12-FLOOD Sentinel 1 Source Imagery |
License | CC-BY-4.0 |
Collection ID | sen12floods_s1_source |
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Description | SEN12-FLOOD Sentinel 2 Source Imagery |
License | CC-BY-4.0 |
Collection ID | sen12floods_s2_source |
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Labels Collections
Description | SEN12-FLOOD Sentinel 1 Labels |
License | CC-BY-4.0 |
Collection ID | sen12floods_s1_labels |
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Description | SEN12-FLOOD Sentinel 2 Labels |
License | CC-BY-4.0 |
Collection ID | sen12floods_s2_labels |
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