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BreizhCrops: A Time Series Dataset for Crop Type Mapping
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We present Breizhcrops, a novel benchmark dataset for the supervised classification of field crops from satellite time series. We aggregated label data and Sentinel-2 top-of-atmosphere as well as bottom-of-atmosphere time series in the region of Brittany (Breizh in local language), north-east France. We compare seven recently proposed deep neural networks along with a Random Forest baseline. The dataset, model (re-)implementations and pre-trained model weights are available at the associated GitHub repository (https://github.com/dl4sits/BreizhCrops) that has been designed with applicability for practitioners in mind. We plan to maintain the repository with additional data and welcome contributions of novel methods to build a state-of-the-art benchmark on methods for crop type mapping.
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Farm-Level, In-Season Crop Identification for India
A Google DeepMind team built a transformer-based system that maps 12 crops across India at farm level, in-season, with state-level area agreement of 94% (winter) and 75% (monsoon) against the 2023-24 census.
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