Semantic Segmentation of Crop Type in South Sudan

However, little work has applied existing methods to these data scarce environments, which also have unique challenges of irregularly shaped fields, frequent cloud coverage, small plots, and a severe lack of training data. To address this gap in the literature, we provide the first crop type semantic segmentation dataset of small holder farms, specifically in Ghana and South Sudan.

We are also the first to utilize high resolution, high frequency satellite data in segmenting small holder farms. The dataset includes time series of satellite imagery from Sentinel-1, Sentinel-2, and PlanetScope satellites throughout 2016 and 2017. For each tile/chip in the dataset, there are time series of imagery from each of the satellites, as well as a corresponding label that defines the crop type at each pixel.