2019 Mali CropType Training Data
This dataset produced by the NASA Harvest team includes crop types labels from ground referencing matched with time-series of Sentinel-2 imagery during the growing season. Ground reference data are collected using an ODK app. Crop types include Maize, Millet, Rice and Sorghum. Labels are vectorized over the Sentinel-2 grid, and provided as raster files. Funding for this dataset is provided by Lutheran World Relief, Bill & Melinda Gates Foundation, and University of Maryland NASA Harvest program.
Dataset ID | umd_mali_crop_type |
DOI | 10.34911/rdnt.tgz68o |
Creator | University of Maryland |
Contact | cnakalem@umd.edu |
Documentation
Citation
Nakalembe, C.L., Ouedraogo, H., Diarra, N., & Kuzimbu, B. (2021). 2019 Mali Crop Type Training Data for Machine Learning (Version 1.0) Radiant MLHub. [Date Accessed] https://doi.org/10.34911/rdnt.tgz68o
Python Client example
from radiant_mlhub import Dataset
ds = Dataset.fetch('umd_mali_crop_type')
for c in ds.collections:
print(c.id)
Download Dataset
Source Imagery Collections
Description | 2019 Mali CropType Training Data Source Imagery |
License | CC-BY-4.0 |
Collection ID | umd_mali_crop_type_source |
Download | |
Labels Collections
Description | 2019 Mali CropType Training Data Labels |
License | CC-BY-4.0 |
Collection ID | umd_mali_crop_type_labels |
Download | |
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