Geospatial Foundation Models Improve Atoll Island Ecosystem Mapping: A Case Study Using AlphaEarth Embeddings

Abstract

Ecosystems and the services they provide are essential for life but continue to undergo degradation worldwide. Satellite remote sensing has been essential for environmental mapping for decades, but can suffer poor accuracy when applied to mapping terrestrial ecosystems. Geospatial Foundation Models (GeoFMs) integrate diverse spatial data, including image data, spatial context, and temporal dynamics, and output readily available covariates called embeddings. GeoFM embeddings likely possess an improved ability to detect ecosystems over traditional approaches. In this study, we investigate whether the use of embeddings from Google’s AlphaEarth Foundations model yields improvements to ecosystem maps developed in the Republic of Maldives compared to single-date Sentinel-2 satellite imagery. We compare (1) per-class accuracies, (2) the effect of decreasing numbers of map classes on overall accuracy, and (3) the relationships between confidence, accuracy, and the number of training samples for each class. AlphaEarth outperforms Sentinel-2 (1) for individual ecosystems, (2) with increasing numbers of classes, and (3) with fewer training data samples while also being more confident in its classifications. We expect that these advantages will promote rapid uptake and expansion in the use of GeoFMs to address challenging spatial analyses, such as mapping global ecosystems and landscapes with limited available data.