Scalable Geospatial Data Generation Using AlphaEarth Foundations Model
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912539616477184 |
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| author | Houriez, Luc Pilarski, Sebastian Vahedi, Behzad Ahmadalipour, Ali Scully, Teo Honda Aflitto, Nicholas Andre, David Jaffe, Caroline Wedner, Martha Mazzola, Rich Jeffery, Josh Messinger, Ben McGinley-Smith, Sage Russell, Sarah |
| author_facet | Houriez, Luc Pilarski, Sebastian Vahedi, Behzad Ahmadalipour, Ali Scully, Teo Honda Aflitto, Nicholas Andre, David Jaffe, Caroline Wedner, Martha Mazzola, Rich Jeffery, Josh Messinger, Ben McGinley-Smith, Sage Russell, Sarah |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11739 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scalable Geospatial Data Generation Using AlphaEarth Foundations Model Houriez, Luc Pilarski, Sebastian Vahedi, Behzad Ahmadalipour, Ali Scully, Teo Honda Aflitto, Nicholas Andre, David Jaffe, Caroline Wedner, Martha Mazzola, Rich Jeffery, Josh Messinger, Ben McGinley-Smith, Sage Russell, Sarah Machine Learning Computer Vision and Pattern Recognition I.4.6; I.5.5 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. |
| title | Scalable Geospatial Data Generation Using AlphaEarth Foundations Model |
| topic | Machine Learning Computer Vision and Pattern Recognition I.4.6; I.5.5 |
| url | https://arxiv.org/abs/2508.11739 |