PLANTEUR: A Crop-Type Segmentation Benchmark for Tropical Regions

Abstract:We introduce PLANTEUR, a benchmark for crop-type mapping from satellite images in the French overseas territories. Building on the PASTIS design, PLANTEUR provides georeferenced and aligned Sentinel-2, Sentinel-1, and Landsat-8 sequences with parcel-derived labels spanning six tropical regions and an 8-class label space. We define both a standard test split and a leave-one-island-out protocol to measure cross-territory generalization, and evaluate fully supervised models, linear probes, and fine-tuned foundation models. The benchmark code and data are open source and available on GitHub and Hugging Face.

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

arXiv:2611.05223 [cs.CV]

(or arXiv:2611.05223v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2611.05223 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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