First Experiences with the AlphaEarth Foundations Model: A Cosine Distance-Based Evaluation

Abstract

The recently released AlphaEarth Foundations Model was generated using multi-source satellite data (optical, radar, LiDAR, etc.) and consists of a 64-dimensional normalized vector for each 10m by 10m pixel on the whole globe for each year since 2017. This representation enables the use of cosine distance to quantify similarity between pixels in the parameter space. The model has been made available in Google Earth Engine, providing new opportunities for environmental monitoring. In this study, we revisited previous analyses of agricultural areas and bee pastures to evaluate the applicability of the new methodology. We compared its performance with earlier approaches based on machine learning classifiers and spectral indices calculations. Our results show that, in most cases, computations were significantly faster and required less code, while accuracy improved slightly compared to traditional methods. We also examined the spatial and temporal transferability of the approach, emphasizing its potential for broader applications in land monitoring.