Radiant MLHub

LandCoverNet

satellite

LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-2 mission in 2018. Version 1.0 of the dataset contains data across Africa, which accounts for ~1/5 of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice.

There are a total of 1980 image chips of 256 x 256 pixels in V1.0 spanning 66 tiles of Sentinel-2. Each image chip contains temporal observations from Sentinel-2 surface reflectance product (L2A) at 10m spatial resolution and an annual class label, all stored in a raster format (GeoTIFF files).

Radiant Earth Foundation designed and generated this dataset with a grant from Schmidt Futures and in kind technology support from Sinergise.

Dataset ID

landcovernet_v1

DOI

10.34911/rdnt.d2ce8i

Creator

Radiant Earth Foundation

Contact

ml@radiant.earth

Documentation

Citation

Alemohammad S.H., Ballantyne A., Bromberg Gaber Y., Booth K., Nakanuku-Diggs L., & Miglarese A.H. (2020) "LandCoverNet: A Global Land Cover Classification Training Dataset", Version 1.0, Radiant MLHub. [Date Accessed] https://doi.org/10.34911/rdnt.d2ce8i

Python Client example

from radiant_mlhub import Dataset

ds = Dataset.fetch('landcovernet_v1')
for c in ds.collections:
    print(c.id)

Python Client quick-start guide

Source Imagery Collection

Description

LandCoverNet Source Imagery

License

CC-BY-4.0

Collection ID

ref_landcovernet_v1_source

Download

Labels Collection

Description

LandCoverNet Labels

License

CC-BY-4.0

Collection ID

ref_landcovernet_v1_labels

Download