# Evaluating AlphaEarth Foundations Embeddings for Wildfire Susceptibility Mapping

arXiv: https://arxiv.org/abs/2608.12663 · HTML: https://mlsanity.com/paper/2608.12663/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 75 references that are in the map.
- First posted on arXiv 12 Aug 2026.

## Builds on (most cited first, 9 of 9)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications](https://mlsanity.com/paper/2412.02732/) (2024)
- [A review of machine learning applications in wildfire science and management](https://mlsanity.com/paper/2003.00646/) (2020)
- [Harvesting AlphaEarth: Benchmarking the Geospatial Foundation Model for Agricultural Downstream Tasks](https://mlsanity.com/paper/2601.00857/) (2025)
- [Physically Interpretable AlphaEarth Foundation Model Embeddings Enable LLM-Based Land Surface Intelligence](https://mlsanity.com/paper/2602.10354/) (2026)
- [Any Model, Any Place, Any Time: Get Remote Sensing Foundation Model Embeddings On Demand](https://mlsanity.com/paper/2602.23678/) (2026)
- [What on Earth is AlphaEarth? Hierarchical structure and functional interpretability for global land cover](https://mlsanity.com/paper/2603.16911/) (2026)
- [Deep Learning for Wildfire Risk Prediction: Integrating Remote Sensing and Environmental Data](https://mlsanity.com/paper/2405.01607/) (2024)

## Built on by (most cited first, 1 of 1)

- [When local gains fail to transfer: Frozen Earth-observation embeddings across wildfires](https://mlsanity.com/paper/2609.34602/) (2026)

## Abstract (arXiv)

Wildfire susceptibility mapping typically relies on physical variables assembled from multiple remote-sensing, climate, and geospatial products. AlphaEarth Foundations (AEF) provides analysis-ready geospatial embeddings that may reduce this dependence on heavy harmonisation and task-specific feature engineering, but their value for wildfire susceptibility mapping has not been systematically evaluated. Using Victoria, Australia (2017-2025), as a case study, we show that AEF embeddings can reconstruct commonly used variables in wildfire susceptibility analysis with high accuracy. In downstream susceptibility models trained on satellite-derived fire occurrence data, embedding-based susceptibility models achieve ROC-AUC values above 0.92 and consistently identify high wildfire susceptibility across eastern Victoria, particularly Gippsland and the north-eastern uplands, with additional localized hotspots in central and northwestern Victoria. A key feature of AEF embeddings is their strong near-region transferability within climatically similar regions. When embedding-based models trained in Victoria are applied to Canberra and Western Sydney-Blue Mountains, ROC-AUC improves by around 4% at Canberra and declines by around 2% at Western Sydney-Blue Mountains, compared with a mean decrease of approximately 25% for physical-variable models. These findings provide practical guidance for using AEF embeddings and lay a foundation for scalable wildfire susceptibility mapping workflows for downstream users such as government agencies and (re)insurers.