Project

# Auto3Dscapes - Autonomous 3D Earth Observation of Dynamic Landscapes

Goal: Project website: http://uni-heidelberg.de/auto3Dscapes
Related blog posts: http://k1z.blog.uni-heidelberg.de/tag/Auto3Dscapes/

The main objective of the project is to develop a computational method for autonomous 3D Earth observation which interlinks the decoupled processes of surveying and data processing by integrating observed Earth surface dynamics as feedback. This approach enables immediate automatic adaptation of the 3D observation strategy (acquisition and analysis) to account for the specific spatiotemporal properties that have been obtained from the accumulating time series datasets. This dynamic adaptation requires automatic 3D geospatial data processing and analysis.

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## Project log

Extracting accumulation and erosion from near-continuous 3D observation of a natural scene is an important step in many geoscientific analyses. Change forms are typically detected and quantified via pairwise 3D surface changes. Surface increase or decrease with a duration over multiple acquisitions may not be detected if corresponding changes are small between successive epochs. Analyzing longer timespans performs poorly where coinciding change processes lead to superimposed change signals. 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 ( $\mu = -25\%$ , $\sigma = 20\%$ deviation to the reference) can be improved in the future by refining the detected start and end times in the automatic approach. The baseline 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 therefore essential for fully automatic change analysis of near-continuous 3D time series as acquired in geographic monitoring settings.
Near-continuously acquired terrestrial laser scanning (TLS) data contains valuable information on natural surface dynamics. An important step in geographic analyses is to detect different types of changes that can be observed in a scene. For this, spatiotemporal segmentation is a time series-based method of surface change analysis that removes the need to select analysis periods, providing so-called 4D objects-by-change (4D-OBCs). This involves higher computational effort than pairwise change detection, and efforts scale with (i) the temporal density of input data and (ii) the (variable) spatial extent of delineated changes. These two factors determine the cost and number of Dynamic Time Warping distance calculations to be performed for deriving the metric of time series similarity. We investigate how a reduction of the spatial and temporal resolution of input data influences the delineation of twelve erosion and accumulation forms, using an hourly five-month TLS time series of a sandy beach. We compare the spatial extent of 4D-OBCs obtained at reduced spatial (1.0 m to 15.0 m with 0.5 m steps) and temporal (2 h to 96 h with 2 h steps) resolution to the result from highest-resolution data. Many change delineations achieve acceptable performance with ranges of ±10 % to ±100 % in delineated object area, depending on the spatial extent of the respective change form. We suggest a locally adaptive approach to identify poor performance at certain resolution levels for the integration in a hierarchical approach. Consequently, the spatial delineation could be performed at high accuracy for specific target changes in a second iteration. This will allow more efficient 3D change analysis towards near-realtime, online TLS-based observation of natural surface changes.
This press release by Heidelberg University features our research about 4D geography for "Understanding the Spatial and Temporal Dimensions of Landscape Dynamics": https://www.uni-heidelberg.de/en/newsroom/understanding-the-spatial-and-temporal-dimensions-of-landscape-dynamics

Anders, K., Winiwarter, L., Mara, H., Lindenbergh, R., Vos, S. E., Höfle, B. (2021). Fully automatic spatiotemporal segmentation of 3D LiDAR time series for the extraction of natural surface changes. ISPRS Journal of Photogrammetry and Remote Sensing, 173, pp. 297-308. doi: 10.1016/j.isprsjprs.2021.01.015.

Geographic observation benefits from the increasing availability of time series of 3D geospatial data, which allow analysis of change processes at high temporal detail and over extensive periods. In this context, the demand for advanced methods to detect and extract topographic surface changes from these 4D geospatial data emerges. Changes in natural scenes occur with varying magnitude, duration, spatial extent, and change rate, and the timing of their occurrence is not known. Standard pairwise change detection requires the selection of fixed analysis periods and the specification of magnitude thresholds to determine accumulation or erosion forms. In settings with continuous surface morphology and dynamic changes to the surface due to material transport, such change forms are typically temporary and may be missed or aggregated if they occur with spatial and/or temporal overlap. This is overcome with the extraction of 4D objects-by-change (4D-OBCs). These objects are obtained by firstly detecting surface changes in the temporal domain at locations in the scene. Subsequently, they are spatially delineated by considering the full history of surface change during region growing from the seed location of a detected change. To perform this spatiotemporal segmentation systematically for entire 3D time series, we develop a fully automatic approach of seed detection and selection, combined with locally adaptive thresholding for region growing of individual objects with varying change properties. We apply our workflow to a five-months hourly time series of around 3,000 terrestrial laser scanning point clouds acquired for coastal monitoring at a sandy beach in The Netherlands. This provides 2,021 4D-OBCs as extracted accumulation or erosion forms. Results are validated through majority agreement of six expert analysts, who evaluate the seg-mentation performance at sample locations throughout the scene. Accordingly, our method extracts surface changes with an error of omission of 4.7% and an error of commission of 16.6%. We examine the results and provide considerations how postprocessing of segments can further improve the change analysis workflow. The developed approach thereby provides a powerful tool for automatic change analysis in 4D geospatial data, namely to detect and delineate natural surface changes across space and time.
Abstract: High-resolution 3D time series data of the surface can be used to analyze spatiotemporal morphologic changes of the snow cover. For this, a time series of the snow cover below the UFS was acquired hourly over several days with a terrestrial laser scanner. Using this dataset, a statistical method is developed for morphological change analysis to automatically derive information on spatial and temporal dynamics of the snow surface. This contributes to a novel approach of autonomous long-term monitoring of topographic change. Zusammenfassung: Mit einer hochaufgelösten 3D-Zeitserie der Oberfläche kann die morphologische Schneedeckenveränderung in ihrer raumzeitlichen Variabilität untersucht werden. Dazu wurde die Schneedecke unterhalb der UFS über mehrere Tage stündlich mit einem terrestrischen Laserscanner aufgenommen. Mit den Daten wird eine statistische Methode entwickelt, um automatisiert Informationen über die Dynamik der Schneedecke abzuleiten. Dies trägt zu einem neuen Ansatz für die autonome Langzeit-Beobachtung von topographischen Veränderungen bei.
This new video presents the challenges of 3D Earth observation and our advances in 4D change analysis in the frame of the Auto3Dscapes project: https://youtu.be/Fdwq-Cp0mFY

Zeitserien von 3D-Punktwolken werden zunehmend für die Beobachtung geomorphologischer Phänomene genutzt. Dieser Beitrag untersucht, wie eine veränderte zeit-liche Auflösung die raumzeitliche Abgrenzung von Oberflächenprozessen beeinflusst. Dazu wird eine Zeitserien-basierte Region Growing Segmentierung verwendet. Die Untersuchung erfolgt am Beispiel einer Sandbank an einem Strand in den Niederlanden, der über fünf Mo-nate stündlich mit terrestrischem Laserscanning erfasst wurde. Die Ergebnisse zeigen, dass die zeitliche Prozessabgrenzung maßgeblich vom gewählten Zeitintervall abhängt. Auf Basis dieser Prozessabgrenzung kann die anschließende raumzeitliche Segmentierung auf ausge-dünnten Zeitserien und somit mit reduziertem Berechnungsaufwand erfolgen.