Scalable Geospatial Data Generation Using AlphaEarth Foundations Model

• URL: http://arxiv.org/abs/2508.11739v1
• Date: Fri, 15 Aug 2025 17:09:48 GMT
• Title: Scalable Geospatial Data Generation Using AlphaEarth Foundations Model
• Authors: Luc Houriez, Sebastian Pilarski, Behzad Vahedi, Ali Ahmadalipour, Teo Honda Scully, Nicholas Aflitto, David Andre, Caroline Jaffe, Martha Wedner, Rich Mazzola, Josh Jeffery, Ben Messinger, Sage McGinley-Smith, Sarah Russell,
• Abstract summary: We propose and evaluate a methodology which leverages Google DeepMind's AlphaEarth Foundations (AEF) to extend geospatial labeled datasets beyond their initial geographic regions.<n>We show that even basic models like random forests or logistic regression can be used to accomplish this task.<n>We investigate a case study of extending LANDFIRE's Existing Vegetation Type (EVT) dataset beyond the USA into Canada at two levels of granularity: EvtPhys (13 classes) and EvtGp (80 classes)
• Score: 0.1775251182905249
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-quality labeled geospatial datasets are essential for extracting insights and understanding our planet. Unfortunately, these datasets often do not span the entire globe and are limited to certain geographic regions where data was collected. Google DeepMind's recently released AlphaEarth Foundations (AEF) provides an information-dense global geospatial representation designed to serve as a useful input across a wide gamut of tasks. In this article we propose and evaluate a methodology which leverages AEF to extend geospatial labeled datasets beyond their initial geographic regions. We show that even basic models like random forests or logistic regression can be used to accomplish this task. We investigate a case study of extending LANDFIRE's Existing Vegetation Type (EVT) dataset beyond the USA into Canada at two levels of granularity: EvtPhys (13 classes) and EvtGp (80 classes). Qualitatively, for EvtPhys, model predictions align with ground truth. Trained models achieve 81% and 73% classification accuracy on EvtPhys validation sets in the USA and Canada, despite discussed limitations.

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