Landslide Hazard Mapping with Geospatial Foundation Models: Geographical Generalizability, Data Scarcity, and Band Adaptability

TL;DR

This paper introduces a geospatial foundation model framework for landslide mapping that enhances generalizability across regions, handles data scarcity effectively, and adapts to different spectral bands, outperforming existing models.

Contribution

It presents a three-axis analytical framework for adapting geospatial foundation models, demonstrating improved robustness and scalability in landslide hazard mapping across diverse datasets and conditions.

Findings

Outperforms task-specific CNNs and vision transformers in landslide mapping.

Maintains accuracy with limited labeled data and spectral variations.

Generalizes reliably across different geographic regions.

Abstract

Landslides cause severe damage to lives, infrastructure, and the environment, making accurate and timely mapping essential for disaster preparedness and response. However, conventional deep learning models often struggle when applied across different sensors, regions, or under conditions of limited training data. To address these challenges, we present a three-axis analytical framework of sensor, label, and domain for adapting geospatial foundation models (GeoFMs), focusing on Prithvi-EO-2.0 for landslide mapping. Through a series of experiments, we show that it consistently outperforms task-specific CNNs (U-Net, U-Net++), vision transformers (Segformer, SwinV2-B), and other GeoFMs (TerraMind, SatMAE). The model, built on global pretraining, self-supervision, and adaptable fine-tuning, proved resilient to spectral variation, maintained accuracy under label scarcity, and generalized more reliably across diverse datasets and geographic settings. Alongside these strengths, we also highlight remaining challenges such as computational cost and the limited availability of reusable AI-ready training data for landslide research. Overall, our study positions GeoFMs as a step toward more robust and scalable approaches for landslide risk reduction and environmental monitoring.