BreizhCrops: A Time Series Dataset for Crop Type Mapping
This provides a new dataset and benchmark for researchers and practitioners in agricultural remote sensing, but it is incremental as it builds on existing methods without major innovations.
The authors introduced BreizhCrops, a benchmark dataset for crop type classification from satellite time series in Brittany, France, and compared seven deep neural networks with a Random Forest baseline, providing open-source code and pre-trained models for practical use.
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.