Local Intrinsic Dimensionality (LID) has shown strong potential for anomaly detection in high-dimensional data, including landslide failure detection in granular media, where early and accurate identification of failure zones is crucial for effective geohazard mitigation. However, this task is still challenging due to the spatial correlations and temporal dynamics that are inherently present in surface displacement data. To address this gap, we propose a novel unsupervised framework called spatiotemporal LID (st-LID) that generalizes the LID for robust failure detection in landslide monitoring networks. Our approach introduces three key innovations: (1) Kinematic enhancement, incorporating velocity into the LID computation to capture instantaneous deformation rates and short-term temporal dynamics; (2) Bayesian spatial fusion, which aggregates LID values across spatial neighborhoods via Bayesian estimation, to embed spatial correlations and account for localized noise; and (3) Temporal modeling (t-LID), a new variant that characterizes long-term dynamics of displacement data, providing a robust temporal representation of displacement behavior. By unifying these components, st-LID identifies complex, multi-stage failure zones often overlooked by existing methods. Extensive experiments show that st-LID consistently outperforms state-of-the-art unsupervised baselines in detection precision and lead-time, providing a robust foundation for landslide early warning systems and targeted risk intervention to enhance community resilience and preparedness strategies.

翻译：局部本质维度（LID）在高维数据异常检测中展现出强大潜力，包括颗粒介质中的滑坡破坏检测——早期准确识别破坏区域对于有效减轻地质灾害至关重要。然而，由于地表位移数据固有的空间相关性和时间动态性，该任务仍具有挑战性。为解决这一不足，我们提出了一种名为时空局部本质维度（st-LID）的新型无监督框架，将LID泛化用于滑坡监测网络中的鲁棒破坏检测。该方法包含三项关键创新：（1）运动学增强：将速度引入LID计算以捕获瞬时变形速率和短期时间动态；（2）贝叶斯空间融合：通过贝叶斯估计聚合空间邻域内LID值，嵌入空间相关性并考虑局部噪声；（3）时间建模（t-LID）：一种描述位移数据长期动态的新变体，提供位移行为的鲁棒时间表征。通过整合这些组件，st-LID能够识别现有方法常忽略的复杂多阶段破坏区域。大量实验表明，st-LID在检测精度和提前时间上始终优于最先进的无监督基线方法，为滑坡早期预警系统和针对性风险干预提供了鲁棒基础，从而增强社区韧性和备灾策略。