Radiant MLHub

A Fusion Dataset for Crop Type Classification in Western Cape, South Africa

satellite

This dataset contains ground reference crop type labels and multispectral and synthetic aperture radar (SAR) imagery from multiple satellites in an area located in Western Cape, South Africa. There are five crop types from the year 2017: Wheat, Barely, Canola, Lucerne/Medics, Small grain grazing. The AOI is split to three tiles. Two tiles are provided as training labels, and one tile will be used for scoring in the competition.

Input imagery consist of time series of Sentinel-2, Sentinel-1 and Planet Fusion (daily and 5-day composite) data. You can access each source from a different collection.

The Planet fusion data are made available under a CC-BY-SA license. As an exception to the AI4EO Terms and Conditions published on the competition website, you confirm, by participating in it, that you agree that your results will be made public under the same, open-source license.

The Western Cape Department of Agriculture (WCDoA) vector data are supplied via Radiant Earth Foundation with limited distribution rights. Data supplied by the WCDoA may not be distributed further or used for commercial purposes. The vector data supplied are intended strictly for use within the scope of this remote sensing competition - for the purpose of academic research to our mutual benefit. The data is intended for research purposes only and the WCDoA cannot be held responsible for any errors or omissions which may occur in the data.

Dataset ID

ref_fusion_competition_south_africa

DOI

10.34911/rdnt.gqy868

Creator

Western Cape Department of Agriculture, Planet, DLR, Sinergise, Radiant Earth Foundation

Contact

ml@radiant.earth

Documentation

Citation

Planet, Radiant Earth Foundation, Western Cape Department of Agriculture, & German Aerospace Center (DLR). (2021). A Fusion Dataset for Crop Type Classification in Western Cape, South Africa (Version 1.0) [Date Accessed]. Radiant MLHub. https://doi.org/10.34911/rdnt.gqy868

Python Client example

from radiant_mlhub import Dataset

ds = Dataset.fetch('ref_fusion_competition_south_africa')
for c in ds.collections:
    print(c.id)

Python Client quick-start guide

Source Imagery Collections

Description

Train Planet Source Imagery

License

CC-BY-SA-2.0

Collection ID

ref_fusion_competition_south_africa_train_source_planet

Download

Description

Train Planet 5 Day Source Imagery

License

CC-BY-SA-2.0

Collection ID

ref_fusion_competition_south_africa_train_source_planet_5day

Download

Description

Train Sentinel-1 Source Imagery

License

CC-BY-4.0

Collection ID

ref_fusion_competition_south_africa_train_source_sentinel_1

Download

Description

Train Sentinel-2 Source Imagery

License

CC-BY-4.0

Collection ID

ref_fusion_competition_south_africa_train_source_sentinel_2

Download

Description

Test Planet Source Imagery

License

CC-BY-SA-2.0

Collection ID

ref_fusion_competition_south_africa_test_source_planet

Download

Description

Test Planet 5 Day Source Imagery

License

CC-BY-SA-2.0

Collection ID

ref_fusion_competition_south_africa_test_source_planet_5day

Download

Description

Test Sentinel-1 Source Imagery

License

CC-BY-4.0

Collection ID

ref_fusion_competition_south_africa_test_source_sentinel_1

Download

Description

Test Sentinel-2 Source Imagery

License

CC-BY-4.0

Collection ID

ref_fusion_competition_south_africa_test_source_sentinel_2

Download

Labels Collections

Description

Train Labels

License

CC-BY-NC-SA-4.0

Collection ID

ref_fusion_competition_south_africa_train_labels

Download

Description

Test Labels

License

CC-BY-NC-SA-4.0

Collection ID

ref_fusion_competition_south_africa_test_labels

Download