Unsupervised Deep Clustering on Spatiotemporal Objects Extracted from 4D Point Clouds for Automatic Identification of Topographic Processes in Natural Environments

We present a method to extract surface change information from 4D time series of topographic point clouds recorded with a terrestrial laser scanner. The method uses sensor information to spatially and temporally smooth the data, reducing uncertainties. The Kalman filter used for the temporal smoothing also allows us to interpolate over data gaps or extrapolate into the future. Clustering areas where change histories are similar allows us to identify processes that may have the same causes.