Towards Improved Crop Type Classification: a Compact Embedding Approach Suitable for Small Fields

Abstract. Satellite -based crop classification and maps are important tools for food security and climate change mitigation, but existing approaches are not effective for small field systems. To address this, crop type classification using embeddings generated by a global foundation model, TESSERA, are compared to standard classification approaches in the literature. We find that our embedding - based approach offers a triple win: 1) consistent and statistically significant performance improvement over current methods, 2) greater simplicity due to the elimination of feature engineering, and 3) the reduction of computational cost. Our embedding -based approach achieves significantly higher F1 scores in the classification of 5 of 7 crop types for small fields in Austria (over 10% improvement in one case). Additionally, the TESSERA embedding -based method uses 8% of compute compared to the raw data method. These results indicate that embeddings are an effective approach for crop type classification tasks in small field systems.