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.
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
from radiant_mlhub import Dataset ds = Dataset.fetch('cgiar_east_africa_agricultural_field_centers') for c in ds.collections: print(c.id)
East Africa Agricultural Field Center Planet Source
East Africa Agricultural Field Center Labels