CV4A Kenya Crop Type CompetitionThis dataset was produced as part of the Crop Type Detection competition at the Computer Vision for Agriculture (CV4A) Workshop at the ICLR 2020 conference. The objective of the competition was to create a machine learning model to classify fields by crop type from images collected during the growing season by the Sentinel-2 satellites.Published on 5 Jul 2023Public
Great African Food Company Crop Type TanzaniaThis dataset contains field boundaries and crop types from farms in Tanzania. Great African Food Company used Farmforce app to collect a point within each field, and recorded other properties including area of the field.Published on 5 Jul 2023Public
Dalberg Data Insights Crop Type UgandaThis dataset contains crop types and field boundaries along with other metadata collected in a campaign run by Dalberg Data Insights in the end of September 2017, as close as possible to the harvest period of 2017. GeoODKapps were used to collect approximately four points per field to get widest coverage during two field campaigns.Published on 5 Jul 2023Public
AgriFieldNet Competition DatasetThis dataset contains crop types of agricultural fields in four states of Uttar Pradesh, Rajasthan, Odisha and Bihar in northern India. Ground reference data for this dataset is collected by IDinsight's Data on Demand team. Radiant Earth Foundation carried out the training dataset curation and publication. This training dataset is generated through a grant from the Enabling Crop Analytics at Scale (ECAAS) Initiative funded by The Bill & Melinda Gates Foundation and implemented by Tetra Tech.Published on 26 Jun 2024Public
Sentinel-2 Cloud Cover Segmentation DatasetIn many uses of multispectral satellite imagery, clouds obscure what we really care about - for example, tracking wildfires, mapping deforestation, or monitoring crop health. Being able to more accurately remove clouds from satellite images filters out interference, unlocking the potential of a vast range of use cases.Published on 26 Jun 2024Public
LandCoverNetLandCoverNet 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.Published on 26 Jun 2024Public
South Africa Crop Type CompetitionThis dataset was produced as part of the Radiant Earth Spot the Crop Challenge (https://zindi.africa/hackathons/radiant-earth-spot-the-crop-hackathon). The objective of the competition was to create a machine learning model to classify fields by crop type from images collected during the growing season by the Sentinel-2 and Sentinel-1 satellites.Published on 26 Jun 2024Public