GPS Coordinates of 18,482 Crop Fields in East Africa with Improved Accuracy using Planet Imagery and Yolo v5 Object Detection Model

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. We prepared and hosted a competition on Zindi (https://zindi.africa) where competitors model the problem using state-of-the-art data science techniques. We provided the competitors with satellite images of fields along with their corresponding manually annotated correct centers. Additionally, we also provided approximate plot size and measured yield in case these help with creating their solutions. Original positions are considered images' centers as (0,0) and a displacement vector for each field in the training set was provided. The goal of the competition was to predict these vectors for each vector in the test set. This dataset includes the locations of 18,481 crop fields across Kenya, Tanzania, and Rwanda, collected in 2016-2017 with mixed qualities and their error-corrected ones from the winning solution using Planet satellite imagery and the Yolo v5 object detection model.