On the Generalizability of Foundation Models for Crop Type Mapping

Foundation models pre-trained using self-supervised learning have shown powerful transfer learning capabilities on various downstream tasks, including language understanding, text generation, and image recognition. The Earth observation (EO) field has produced several foundation models pre-trained directly on multispectral satellite imagery for applications like precision agriculture, wildfire and drought monitoring, and natural disaster response. However, few studies have investigated the ability of these models to generalize to new geographic locations, and potential concerns of geospatial bias -- models trained on data-rich developed nations not transferring well to data-scarce developing nations -- remain. We investigate the ability of popular EO foundation models to transfer to new geographic regions in the agricultural domain, where differences in farming practices and class imbalance make transfer learning particularly challenging. We first select five crop classification datasets across five continents, normalizing for dataset size and harmonizing classes to focus on four major cereal grains: maize, soybean, rice, and wheat. We then compare three popular foundation models, pre-trained on SSL4EO-S12, SatlasPretrain, and ImageNet, using in-distribution (ID) and out-of-distribution (OOD) evaluation. Experiments show that pre-trained weights designed explicitly for Sentinel-2, such as SSL4EO-S12, outperform general pre-trained weights like ImageNet. Furthermore, while only 100 labeled images are sufficient for achieving high overall accuracy, 900 images are required to achieve high average accuracy due to class imbalance. All harmonized datasets and experimental code are open-source and available for download.

@article{chang2025_2409.09451,
  title={ On the Generalizability of Foundation Models for Crop Type Mapping },
  author={ Yi-Chia Chang and Adam J. Stewart and Favyen Bastani and Piper Wolters and Shreya Kannan and George R. Huber and Jingtong Wang and Arindam Banerjee },
  journal={arXiv preprint arXiv:2409.09451},
  year={ 2025 }
}