LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet Australia contains data across Australia, which accounts for ~7% 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 600 image chips of 256 x 256 pixels in LandCoverNet Australia V1.0 spanning 20 tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files):
Radiant Earth Foundation designed and generated this dataset with a grant from Schmidt Futures with additional support from NASA ACCESS, Microsoft AI for Earth and in kind technology support from Sinergise.
Dataset ID | ref_landcovernet_au_v1 |
DOI | 10.34911/rdnt.0vgi25 |
Creator | Radiant Earth Foundation |
Contact | ml@radiant.earth |
Radiant Earth Foundation (2022) LandCoverNet Australia: A Geographically Diverse Land Cover Classification Training Dataset, Version 1.0, Radiant MLHub. https://doi.org/10.34911/rdnt.0vgi25
from radiant_mlhub import Dataset ds = Dataset.fetch('ref_landcovernet_au_v1') for c in ds.collections: print(c.id)
Description | LandCoverNet Australia Sentinel 2 Source Imagery |
License | CC-BY-4.0 |
Collection ID | ref_landcovernet_au_v1_source_sentinel_2 |
Description | LandCoverNet Australia Sentinel 1 Source Imagery |
License | CC-BY-4.0 |
Collection ID | ref_landcovernet_au_v1_source_sentinel_1 |
Description | LandCoverNet Australia Landsat 8 Source Imagery |
License | CC-BY-4.0 |
Collection ID | ref_landcovernet_au_v1_source_landsat_8 |
Description | LandCoverNet Australia labels |
License | CC-BY-4.0 |
Collection ID | ref_landcovernet_au_v1_labels |