Radiant MLHub

SEN12-FLOOD : A SAR and Multispectral Dataset for Flood Detection

satellite

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)

Python Client quick-start guide

Source Imagery Collections

Description

SEN12-FLOOD Sentinel 1 Source Imagery

License

CC-BY-4.0

Collection ID

sen12floods_s1_source

Download

Description

SEN12-FLOOD Sentinel 2 Source Imagery

License

CC-BY-4.0

Collection ID

sen12floods_s2_source

Download

Labels Collections

Description

SEN12-FLOOD Sentinel 1 Labels

License

CC-BY-4.0

Collection ID

sen12floods_s1_labels

Download

Description

SEN12-FLOOD Sentinel 2 Labels

License

CC-BY-4.0

Collection ID

sen12floods_s2_labels

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