On the Generalizability of Foundation Models for Crop Type Mapping
This work addresses geospatial bias in crop classification for precision agriculture, particularly in data-scarce developing nations, though it is incremental as it builds on existing foundation models.
The study evaluated the generalizability of Earth observation foundation models for crop type mapping across diverse geographic locations, finding that models pre-trained on Sentinel-2 data (e.g., SSL4EO-S12) outperformed general models like ImageNet, with 100 labeled images achieving high overall accuracy but 900 needed to address class imbalance and improve average accuracy.
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 evaluate three popular EO foundation models, SSL4EO-S12, SatlasPretrain, and ImageNet, on five crop classification datasets across five continents. Results show that pre-trained weights designed explicitly for Sentinel-2, such as SSL4EO-S12, outperform general pre-trained weights like ImageNet. While only 100 labeled images are sufficient for achieving high overall accuracy, 900 images are required to mitigate class imbalance and improve average accuracy.