Clustering Time Series of Repeated Scan Data of Sandy Beaches
TU Delft, Netherlands, The
Sandy beaches are highly dynamic areas affected by different natural and anthropogenic effects. Large changes, caused by a storm for example, are in general well-understood and easy to measure. Most times, only small changes, at the centimeter scale, are occurring, but these changes accumulate significantly over periods from weeks to months. Laser scanning is a suitable technique to measure such small signals, as it is able to obtain dense 3D terrain data at centimeter level in a time span of minutes. In this work we consider two repeated laser scan data sets of two different beaches in The Netherlands. The first data set is from around the year 2000 and consists of six consecutive yearly airborne laser scan data sets of a beach on Texel. The second data set is from 2017 and consists of 30 consecutive daily terrestial scans of a beach near The Hague. So far, little work has been done on time series analysis of repeated scan data. To obtain a first grouping of morphologic processes, we propose to use a simple un-supervised clustering approach, k-means clustering, on de-leveled, cumulative point-wise time series. The results for both regions of interest, obtained using k=5 and k=10 clusters, indicate that such clustering gives a meaningful decomposition of the morphological laser scan data into clusters that exhibit similar change patterns. At the same time, we realize that the chosen approach is just a first step in a wide open topic of clustering spatially correlated long time series of morphological laser scan data as are now obtained by permanent laser scanning.
Non-Rigid Multi-Body Tracking in RGBD Streams
1China Agricultural University, China, People's Republic of; 2Department of Computer Science, Stevens Institute of Technology, New Jersey, USA; 3Department of Land, Environment, Agriculture and Forestry, University of Padova, Italy
To efficiently collect training data for an off-the-shelf object detector, we consider the problem of segmenting and tracking non-rigid objects from RGBD sequences by introducing the spatio-temporal matrix with very few assumptions - no prior object model and no stationary sensor. Spatial temporal matrix is able to encode not only spatial associations between multiple objects, but also component-level spatio temporal associations that allow the correction of falsely segmented objects in the presence of various types of interaction among multiple objects. Extensive experiments over complex human/animal body motions with occlusions and body part motions demonstrate that our approach substantially improves tracking robustness and segmentation accuracy.
High-Frequency 3D Geomorphic Observation using Hourly Terrestrial Laser Scanning Data of a Sandy Beach
13D Geospatial Data Processing Research Group (3DGeo), Institute of Geography, Heidelberg University, Germany; 2Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Germany; 3Department of Geoscience & Remote Sensing, Delft University of Technology, The Netherlands; 4Department of Hydraulic Engineering, Delft University of Technology, The Netherlands; 5Heidelberg Center for the Environment, Heidelberg University, Germany
Geomorphic processes occur spatially variable and at varying magnitudes, frequencies and velocities. This poses a great challenge to current methods of deformation analysis. For this quantification of morphologic change, permanent terrestrial laser scanning (TLS) can generate time series of 3D point clouds at high temporal and spatial resolution. We investigate how the temporal interval influences volume change observed on a sandy beach regarding the temporal detail of the change process and the total volume budget, on which accretion and erosion counteract. We use an hourly time series of TLS point clouds acquired over six weeks in Kijkduin, the Netherlands. A raster-based approach of elevation differencing provides the volume change over time per square meter. We compare the hourly analysis to results of a three-week observation interval. For the larger interval, a volume increase of 0.3 m³/m² is missed on a forming sand bar before it disappears, which corresponds to half its volume. Generally, a strong relationship is shown between observation frequency and observed volume change. An increase from weekly to daily observations leads to a five times larger volume change quantified in total. Another important finding is a temporally variable measurement uncertainty in the 3D time series, which follows the daily course of air temperature. Further experiments are required to fully understand the effect of atmospheric conditions on high-frequency TLS acquisition in beach environments. Continued research of 4D geospatial analysis methods will enable automatic identification of dynamic change processes and improve the understanding of geomorphic deformation.
Assessment of Landslide-Induced Displacement and Deformation of Above-Ground Objects Using UAV-Borne and Airborne Laser Scanning Data
1Institute for Interdisciplinary Mountain Research, Austrian Academy of Sciences, Technikerstr. 21a, 6020 Innsbruck, Austria; 2Institute for Geography, University of Innsbruck, Innrain 52f, 6020 Innsbruck, Austria; 3Federal state of Tyrol, Division of Geoinformation, Herrengasse 3, 6020 Innsbruck, Austria; 4Laserdata GmbH, Technikerstr. 21a, 6020 Innsbruck, Austria
Multi-temporal 3D point clouds acquired with a laser scanner can be efficiently used for an area-wide assessment of landslide-induced surface changes. In the present study, displacements of the Vögelsberg landslide (Tyrol, Austria) are assessed based on available data acquired with airborne laser scanning (ALS) in 2013 and data acquired with an unmanned aerial vehicle (UAV) equipped with a laser scanner (ULS) in 2018. Following the data pre-processing steps including registration and ground filtering, buildings are segmented and extracted from the datasets. The roofs, represented as multi-temporal 3D point clouds are then used to derive displacement vectors with a novel matching tool based on the iterative closest point (ICP) algorithm. The resulting mean annual displacements are compared to the results of a geodetic monitoring based on an automatic tracking total station (ATTS) measuring 53 retroreflective prisms across the study area every hour since May 2016. In general, the results are in agreement concerning the mean annual magnitude (ATTS: 6.4 cm within 2.2 years, 2.9 cm/a; laser scanning data: 13.2 cm within 5.4 years, 2.4 cm/a) and direction of the derived displacements. The analysis of the laser scanning data proved suitable for deriving long-term landslide displacements and can provide additional information about the deformation of single roofs.
Comparison and Time Series Analysis of Landslide Displacement Mapped by Airborne, Terrestrial and Unmanned Aerial Vehicle Based Platforms
1Institute of Interdisciplinary Mountain Research, Austrian Academy of Science, Austria; 2Institute of Geography, University of Innsbruck, Austria; 3Laserdata GmbH, Austria
Slow moving deep-seated gravitational slope deformations are threatening infrastructure and economic wellbeing in mountainous areas. Accelerating landslides may end up in a catastrophic slope failure in terms of rapid rock avalanches. Continuous landslide monitoring enables the identification of critical acceleration thresholds, which are required in natural hazard management. Among many existing monitoring methods, laser scanning is a cost effective method providing 3D data for deriving three dimensional and areawidedisplacement vectors at certain morphological structures travelling on top of the landslide. Comparing displacements between selected observation periods allows the spatial interpretation of landslide acceleration or deceleration. This contribution presents five laser scanning datasets of the active Reissenschuh landslide (Tyrol, Austria) acquired by airborne laser scanning (ALS), terrestrial laser scanning (TLS) and Unmanned aerial vehicle Laser Scanning (ULS) sensors. Three observation periods with acquisition dates between 2008 and 2018 are used to derive area-wide displacement vectors. To ensure a most suitable displacement derivation between ALS, TLS and ULS platforms, an analysis investigating point cloud features within varying search radii is carried out, in order to identify a neighbourhood where common surfaces are represented platform independent or differences between the platforms are minimized. Consequent displacement vector estimation is done by ICP-Matching using morphological structures within the high resolution TLS and ULS point cloud. Displacements from the lower resolution ALS point cloud and TLS point cloud were determined using a modified version of the well-known image correlation (IMCORR) method working with point cloud derived shaded relief images combined with digital terrain models (DTM). The interplatform compatible analyses of the multi-temporal laser scanning data allows to quantify the area-wide displacement patterns of the landslide. Furthermore, changes of these displacement patterns over time are assessed area-wide. Spatially varying areas of landslide acceleration and deceleration in the order of plus minus 15 cm/a between 2008 and 2017 and an area wide acceleration of up to 20 cm/a between 2016 and 2018 are identified. Continuing the existing time series with future ULS acquisitions may enable a more complete and detailed displacement monitoring using entirely represented objects within the point clouds.