arXiv：cs.LG· Jiapan Wang, Daan Hulskemper, Mathilde Letard, Roderik Lindenbergh, Katharina Anders·· 4 小时前AI 评分31
DeepTopoClustering：从 4D 点云无监督推导地表过程分类体系用于地形监测
DeepTopoClustering: Unsupervised Derivation of Surface Process Taxonomy from 4D Point Clouds for Topographic Monitoring
AI 导读
研究者提出 DeepTopoClustering（DTC），一个从 4D 点云的地表活动对象（4D-OBCs）无监督推导层次化过程分类体系的框架。该方法将每个 4D-OBC 转换为 GeoMorphogram，用卷积自编码器学习潜在嵌入并结合层次化深度聚类目标进行联合优化。
正文
Abstract: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.
| Comments: | Submitted to ISPRS Journal of Photogrammetry and Remote Sensing |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.09860 [cs.CV] |
| (or arXiv:2610.09860v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09860 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jiapan Wang [view email]
[v1]
Wed, 7 Oct 2026 11:16:19 UTC (13,473 KB)
来源：arXiv：cs.LG · arxiv.org