CV4A Kenya Crop Type Competition

This 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. <br><br> The ground reference data were collected by the PlantVillage team, and Radiant Earth Foundation curated the training dataset after inspecting and selecting more than 4,000 fields from the original ground reference data.

The dataset has been split into training and test sets (3,286 in the train and 1,402 in the test). <br><br> The dataset is cataloged in four tiles. These tiles are smaller than the original Sentinel-2 tile that has been clipped and chipped to the geographical area that labels have been collected. <br><br> Each tile has a) 13 multi-band observations throughout the growing season. Each observation includes 12 bands from Sentinel-2 L2A product, and a cloud probability layer.