基于地理空间基础模型的滑坡灾害制图：地理泛化性、数据稀缺性与波段适应性
Landslide Hazard Mapping with Geospatial Foundation Models: Geographical Generalizability, Data Scarcity, and Band Adaptability
AI总结：
本文提出传感器、标签、域三轴分析框架，验证Prithvi-EO-2.0在滑坡制图中优于CNN、ViT及其他GeoFMs，在光谱变化、标签稀缺和跨域泛化方面表现更稳健，同时指出计算成本与AI就绪数据不足等挑战。
摘要：
滑坡对生命、基础设施和环境造成严重损害，因此准确及时的制图对于灾害备灾和响应至关重要。然而，传统深度学习模型在跨不同传感器、不同区域或训练数据有限的条件下应用时往往表现不佳。为应对这些挑战，我们提出了一个由传感器、标签和域三个轴构成的分析框架，用于适配地理空间基础模型（GeoFMs），重点研究Prithvi-EO-2.0在滑坡制图中的应用。通过一系列实验，我们表明该模型持续优于任务特定的CNN（U-Net、U-Net++）、视觉Transformer（Segformer、SwinV2-B）以及其他GeoFMs（TerraMind、SatMAE）。该模型建立在全球预训练、自监督和可适配微调的基础上，被证明对光谱变化具有鲁棒性，在标签稀缺条件下保持精度，并在不同数据集和地理环境中更可靠地泛化。除了这些优势之外，我们还指出了仍然存在的挑战，例如计算成本以及可用于滑坡研究的可复用AI就绪训练数据的有限可用性。总体而言，我们的研究将GeoFMs定位为迈向更稳健、可扩展的滑坡风险降低与环境监测方法的一步。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
