用谷歌地球嵌入数据替代传统滑坡因子，提升滑坡易发性预测精度。
From Landslide Conditioning Factors to Satellite Embeddings: Evaluating the Utilisation of Google AlphaEarth for Landslide Susceptibility Mapping using Deep Learning
- 用谷歌地球嵌入数据替代传统滑坡因子，统一表征地表环境。
- 全64通道嵌入使F1-score提升4%~15%，AUC提高0.04~0.11。
- 在台湾南投和意大利艾米利亚表现更优，与数据时间对齐度相关。
基于数据的滑坡易发性映射（LSM）通常依赖滑坡条件因子（LCFs），其可用性、异质性和预处理不确定性会影响制图可靠性。近期，谷歌地球嵌入（Google AlphaEarth, AE）从多源地理空间观测中提取，作为地表状况的统一表征出现。本研究评估了AE嵌入作为LSM替代预测因子的潜力。比较了保留主成分与完整64通道嵌入两种AE表示形式，与传统LCFs在三个区域（台湾南投县、香港、意大利艾米利亚-罗马涅部分区域）的表现，采用三种深度学习模型（CNN1D、CNN2D、Vision Transformer）。通过多种评估指标、ROC-AUC分析、误差统计和空间模式评估性能。结果表明，所有区域和模型中，基于AE的模型均优于传统LCFs，F1分数更高，AUC值更大，误差分布更稳定。使用完整64通道嵌入时改进最显著，F1分数提升约4%至15%，AUC提升0.04至0.11，具体取决于研究区域和模型。基于AE的易发性图与实际滑坡发生位置的空间对应更清晰，对局部易发条件更敏感。在南投和艾米利亚地区改善更明显，表明AE嵌入与滑坡清单的时间对齐越紧密，预测效果越好。这些发现突显了AE嵌入作为标准化、信息丰富的传统LCFs替代方案的巨大潜力。
原文摘要 · Abstract (English)
Data-driven landslide susceptibility mapping (LSM) typically relies on landslide conditioning factors (LCFs), whose availability, heterogeneity, and preprocessing-related uncertainties can constrain mapping reliability. Recently, Google AlphaEarth (AE) embeddings, derived from multi-source geospatial observations, have emerged as a unified representation of Earth surface conditions. This study evaluated the potential of AE embeddings as alternative predictors for LSM. Two AE representations, including retained principal components and the full set of 64 embedding bands, were systematically compared with conventional LCFs across three study areas (Nantou County, Taiwan; Hong Kong; and part of Emilia-Romagna, Italy) using three deep learning models (CNN1D, CNN2D, and Vision Transformer). Performance was assessed using multiple evaluation metrics, ROC-AUC analysis, error statistics, and spatial pattern assessment. Results showed that AE-based models consistently outperformed LCFs across all regions and models, yielding higher F1-scores, AUC values, and more stable error distributions. Such improvement was most pronounced when using the full 64-band AE representation, with F1-score improvements of approximately 4% to 15% and AUC increased ranging from 0.04 to 0.11, depending on the study area and model. AE-based susceptibility maps also exhibited clearer spatial correspondence with observed landslide occurrences and enhanced sensitivity to localised landslide-prone conditions. Performance improvements were more evident in Nantou and Emilia than in Hong Kong, revealing that closer temporal alignment between AE embeddings and landslide inventories may lead to more effective LSM outcomes. These findings highlight the strong potential of AE embeddings as a standardised and information-rich alternative to conventional LCFs for LSM.
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