The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1545–1551, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1545-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1545–1551, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1545-2020

  14 Aug 2020

14 Aug 2020

BREIZHCROPS: A TIME SERIES DATASET FOR CROP TYPE MAPPING

M. Rußwurm1, C. Pelletier2, M. Zollner1, S. Lefèvre2, and M. Körner1 M. Rußwurm et al.
  • 1Chair of Remote Sensing Technology, Department of Aerospace and Geodesy, Technical University of Munich, Germany
  • 2Univ. Bretagne Sud, UMR 6074, IRISA, F-56000 Vannes, France

Keywords: Satellite Image Time Series, Deep Learning, Crop Type Mapping, Dataset, Benchmark, Sentinel-2

Abstract. 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.