Radiant MLHub

East Africa Agricultural Field Centers

satellite

Georeferenced crop yield prediction is a valuable tool for agronomists and policymakers. One challenge with many existing datasets is that of location accuracy. GPS locations for fields can end up offset from the true location due to sensor inaccuracies or from locations being collected at the edges of fields rather than the field centers. This makes it harder to connect remote-sensed data to the yield values. The goal of this project was to produce a method that can help correct these location offsets by finding the most probable field center given an input location.

Dataset ID

cgiar_east_africa_agricultural_field_centers

DOI

10.6084/m9.figshare.15157263.v1browse DOI

Creator

CGIAR

Contact

D.GUERENA@cgiar.org

Documentation

Citation

Amer, Karim; Eissa, Kareem (2021): GPS Coordinates of 18,482 Crop Fields in East Africa with Improved Accuracy using Planet Imagery and Yolo v5 Object Detection Model. figshare. Dataset. https://doi.org/10.6084/m9.figshare.15157263.v1

Python Client example

from radiant_mlhub import Dataset

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

Python Client quick-start guide

Download Dataset

Source Imagery Collection

Description

East Africa Agricultural Field Center Planet Source

License

CC-BY-NC-4.0

Collection ID

cgiar_east_africa_agricultural_field_centers_source_planet

Download

Labels Collection

Description

East Africa Agricultural Field Center Labels

License

CC-BY-NC-4.0

Collection ID

cgiar_east_africa_agricultural_field_centers_labels

Download