On the Generalizability of Foundation Models for Crop Type Mapping

TL;DR

This study evaluates the generalizability of Earth observation foundation models for crop classification across diverse geographic regions, highlighting the importance of specialized pre-training and data quantity.

Contribution

It provides a comparative analysis of EO foundation models' transferability to new locations and examines data requirements for effective crop mapping.

Findings

SSL4EO-S12 outperforms general models like ImageNet for Sentinel-2 data.

100 labeled images suffice for high accuracy, but 900 are needed for balanced class performance.

Models trained on data-rich regions may not transfer well to data-scarce areas.

Abstract

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.