A survey of publicly available multi-temporal point cloud datasets

Multi-temporal point clouds, which capture the same acquisition area at different points in time, enable change analysis and forecasting across various disciplines. Publicly available datasets play an important role in the development and evaluation of such approaches by enhancing comparability and reducing the effort required for data acquisition and preparation. However, identifying suitable datasets, assessing their characteristics, and comparing them with similar ones remains challenging and tedious due to the lack of a centralized distribution and documentation platform. In this paper, we provide a comprehensive overview of publicly available multi-temporal point cloud datasets. We evaluate each dataset across 30 different characteristics, grouped into six categories, and highlight current gaps and future challenges. Our analysis shows that, although many datasets are accompanied by extensive documentation, unclear usage terms and unreliable data hosting can limit their accessibility and adoption. In addition to clear correlations between application domains, acquisition methods, and captured scene types, there is also some overlap in point cloud requirements across domains. However, inconsistencies in file formats, data representations, and labeling practices hinder cross-domain and cross-application reuse. In the context of machine learning, we observe a positive trend towards more labeled datasets. Nevertheless, gaps remain due to limited coverage of natural environments and poor geographic diversity. Although there are already many positive examples of accessible datasets, future dataset publications would benefit from standardized review processes and a stronger focus on accessibility and usability across application areas. • A comprehensive overview of publicly available multi-temporal point cloud datasets. • An assessment of these datasets based on 30 characteristics. • An analysis of current gaps and future challenges based on this assessment. • A website for dataset sorting, filtering, and comparison. • A collection of processing scripts for working with the surveyed datasets.