This dataset comprises the source code (Python scripts) and data to perform spatiotemporal segmentation in time series of surface change data for a (i) synthetic dataset and (ii) hourly snow cover changes acquired by terrestrial laser scanning. Further details are given in the corresponding paper:

Extracting accumulation and erosion from near-continuous 3D observation of a natural scene is an important step in many geoscientific analyses. We examine how spatiotemporal segmentation improves the extraction of change volumes from near-continuous 3D time series by using the full temporal information of surface changes. Synthetic changes and manually derived reference changes from an hourly terrestrial laser scanning time series of snow cover monitoring are detected in the temporal domain and delineated accurately (area intersection over union of 0.86 for snow cover changes). The accuracy of change volumes (mean of -25 %; std. dev. of 20 % deviation to the reference) can be improved in the future by refining the detected start and end times in the fully automatic approach. The established pairwise methods only achieve high quantification accuracies if area and timespans of changes are known a-priori. Incorporating the surface change history in change extraction is thereby shown to be essential for change analysis of near-continuous 3D time series as acquired in geographic monitoring settings.