Local Intrinsic Dimensionality (LID) has shown strong potential for identifying anomalies and outliers in high-dimensional data across a wide range of real-world applications, including landslide failure detection in granular media. Early and accurate identification of failure zones in landslide-prone areas is crucial for effective geohazard mitigation. While existing approaches typically rely on surface displacement data analyzed through statistical or machine learning techniques, they often fall short in capturing both the spatial correlations and temporal dynamics that are inherent in such data. To address this gap, we focus on ground-monitored landslides and introduce a novel approach that jointly incorporates spatial and temporal information, enabling the detection of complex landslides and including multiple successive failures occurring in distinct areas of the same slope. To be specific, our method builds upon an existing LID-based technique, known as sLID. We extend its capabilities in three key ways. (1) Kinematic enhancement: we incorporate velocity into the sLID computation to better capture short-term temporal dependencies and deformation rate relationships. (2) Spatial fusion: we apply Bayesian estimation to aggregate sLID values across spatial neighborhoods, effectively embedding spatial correlations into the LID scores. (3) Temporal modeling: we introduce a temporal variant, tLID, that learns long-term dynamics from time series data, providing a robust temporal representation of displacement behavior. Finally, we integrate both components into a unified framework, referred to as spatiotemporal LID (stLID), to identify samples that are anomalous in either or both dimensions. Extensive experiments show that stLID consistently outperforms existing methods in failure detection precision and lead-time.

翻译：局部本征维度（LID）在包括颗粒介质滑坡失稳检测在内的广泛现实应用中，已显示出识别高维数据异常值和离群点的强大潜力。在滑坡易发区域，对失稳区域进行早期且准确的识别对于有效减轻地质灾害至关重要。现有方法通常依赖于通过统计或机器学习技术分析地表位移数据，但往往难以同时捕捉此类数据固有的空间相关性与时间动态特性。为弥补这一不足，本研究聚焦于地面监测的滑坡，提出一种新颖方法，该方法联合融入空间与时间信息，从而能够检测复杂滑坡，包括在同一斜坡不同区域发生的多次连续失稳。具体而言，我们的方法建立在一种现有的基于LID的技术（称为sLID）之上，并从以下三个关键方面扩展其能力：（1）运动学增强：将速度纳入sLID计算，以更好地捕捉短期时间依赖性与变形速率关系。（2）空间融合：应用贝叶斯估计聚合空间邻域内的sLID值，从而有效地将空间相关性嵌入LID评分中。（3）时间建模：引入一种时间变体tLID，该变体从时间序列数据中学习长期动态，为位移行为提供稳健的时间表征。最后，我们将这两个组件集成到一个统一框架中，称为时空LID（stLID），用以识别在单一或两个维度上呈现异常的样本。大量实验表明，stLID在失稳检测精度和预警提前期方面均持续优于现有方法。