发表机构
Technical University of Munich; Delft University of Technology（慕尼黑工业大学; 代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出DeepTopoClustering无监督框架，利用GeoMorphogram和卷积自编码器从4D点云推导层次化地表过程分类，在沙滩数据集上达到与专家判断高度一致（F1=0.78），实现可解释的自动化分类。
AI 中文摘要
永久性激光扫描（PLS）获取的4D点云能够对动态地形环境中的地表变化进行精确的高频监测。然而，现有方法在将检测到的地表活动组织为有意义的类型方面仍存在局限。我们提出了DeepTopoClustering（DTC），一种无监督框架，用于从基于对象的地表活动中推导出层次化的过程分类体系，这些活动被称为4D变化对象（4D-OBCs）。我们将每个4D-OBC转换为GeoMorphogram，这是一种表示空间有界地表活动内地形变化时间演化的分布序列。卷积自编码器从GeoMorphograms中学习潜在嵌入，并通过层次化深度聚类目标进行联合优化，以将地表活动组织为层次结构。我们使用两个沙滩地点4D数据集及其组合的专家标注来评估所学到的层次结构。带有GeoMorphograms的DTC在包含八种主要过程类型的分类层级上达到了与专家判断的最高一致性（$F_1=0.78$，匹配准确率$=0.92$），优于降维和传统平面聚类。所学到的分类体系分离了主要的侵蚀主导和沉积主导活动，并根据变化幅度、持续时间、紧凑性和时间演化区分了更细的子类型。因此，DTC提供了一条从4D变化检测到数据驱动、专家支持的地表过程分类体系的可扩展且可解释的路径，推进了地形监测中理解地表动态的自动化知识推导。
英文摘要
4D point clouds acquired by permanent laser scanning (PLS) enable accurate high-frequency monitoring of surface change in dynamic topographic environments. However, existing methods remain limited in organizing detected surface activities into meaningful process types. We propose DeepTopoClustering (DTC), an unsupervised framework for deriving a hierarchical process taxonomy from object-based surface activities, so-called 4D objects-by-change (4D-OBCs). We transform each 4D-OBC into a GeoMorphogram, a distributional sequence representing the temporal evolution of topographic change within a spatially bounded surface activity. A convolutional autoencoder learns latent embeddings from GeoMorphograms, which are jointly optimized using a hierarchical deep clustering objective to organize surface activities into a hierarchy. We evaluate the learned hierarchy using expert annotations on two 4D datasets of sandy beach sites and their combination. DTC with GeoMorphograms achieves the highest agreement with expert judgment at the taxonomy level comprising eight major process types ($F_1=0.78$, match accuracy $=0.92$), outperforming dimensionality reduction and conventional flat clustering. The learned taxonomy separates major erosion- and deposition-dominated activities and distinguishes finer subtypes based on change magnitude, duration, compactness, and temporal evolution. DTC thus provides a scalable and interpretable route from 4D change detection to a data-driven, expert-supported surface process taxonomy, advancing automated knowledge derivation for understanding surface dynamics in topographic monitoring.
CommentsSubmitted to ISPRS Journal of Photogrammetry and Remote Sensing
