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.

翻译：数据驱动的滑坡易发性制图通常依赖于滑坡条件因子，但其可用性、异质性及预处理相关的不确定性可能制约制图的可靠性。近期，源自多源地理空间观测数据的Google AlphaEarth嵌入，已成为地球表面状况的统一表征。本研究评估了AE嵌入作为LSM替代预测因子的潜力。在三个研究区域（台湾南投县、香港以及意大利艾米利亚-罗马涅部分地区），使用三种深度学习模型，系统比较了两种AE表征与传统的LCFs。性能通过多种评估指标、ROC-AUC分析、误差统计和空间格局评估进行衡量。结果表明，基于AE的模型在所有区域和模型中均持续优于LCFs，获得了更高的F1分数、AUC值以及更稳定的误差分布。当使用完整的64波段AE表征时，这种改进最为显著，F1分数提升约4%至15%，AUC值增加0.04至0.11，具体取决于研究区域和模型。基于AE的易发性图也与观测到的滑坡事件表现出更清晰的空间对应关系，并对局部易滑坡条件具有更高的敏感性。性能改进在南投和艾米利亚比在香港更为明显，这表明AE嵌入与滑坡编录数据在时间上更紧密的对齐可能带来更有效的LSM结果。这些发现凸显了AE嵌入作为传统LCFs的标准化、信息丰富的替代方案，在滑坡易发性制图中具有巨大潜力。