Cloud-to-cloud (C2C) distance calculations are frequently performed as an initial stage in change detection and spatiotemporal analysis with point clouds. There are various methods for calculating C2C distance, also called inter-point distance, which refers to the distance between two corresponding point clouds captured at different epochs. These methods can be classified from simple to complex, with more steps and calculations required for the latter. Generally, it is assumed that a more complex method will result in a more precise calculation of inter-point distance, but this assumption is rarely evaluated. This paper compares eight commonly used methods for calculating the inter-point distance. The results indicate that the accuracy of distance calculations depends on the chosen method and a characteristic related to the point density, the intra-point distance, which refers to the distance between points within the same point cloud. The results are helpful for applications that analyze spatiotemporal point clouds for change detection. The findings will be helpful in future applications, including analyzing spatiotemporal point clouds for change detection.@en

The advantages of using point clouds for change detection analysis include comprehensive spatial and temporal representation, as well as high precision and accuracy in the calculations. These benefits make point clouds a powerful data type for spatio-temporal analysis. Nevertheless, most current change detection methods have been specifically designed and utilized for raster data. This research aims to identify the most suitable cloud-to-cloud (c2c) distance calculation algorithm for further implementation in change detection for spatio-temporal point clouds. Eight different methods, varying in complexity and execution time, are compared without converting the point cloud data into rasters. Hourly point cloud data from monitoring a beach-dune system's dynamics is used to carry out the comparison. The c2c distance methods are (1) the nearest neighbor, (2) least squares plane, (3) linear interpolation, (4) quadratic (height function), (5) 2.5D triangulation, (6) natural neighbor interpolation (NNI), (7) inverse distance weight (IDW) and (8) multiscale model to model cloud comparison (M3C2). We evaluate these algorithms, considering both the accuracy of the calculated distance and the execution time. The results can be valuable for analyzing and monitoring the (build) environment with spatio-temporal point cloud data.@en

We introduce SARXarray and STMtools, two python libraries designed to support the exploitation of modern Interferometric Synthetic Aperture Radar (InSAR) data by enabling handling of larger-than memory datasets and the incorporation and fusion with relevant contextual information.The libraries are developed upon two innovative and well-established open-source Python libraries:Xarray [5] and Dask [6]. They are implemented as Xarray extensions. SARXarray is designed to manipulate and operate on larger-than-memory coregistered raster stacks such as Single-look Complex images (SLC) or interferograms, performing scatterer selection and producing STM objects. STMtools leverage the Space-Time Matrix (STM) concept [3,4] and provides functionalities to process STM and perform enrichment/data fusion with other data sources.Both libraries are built on the Xarray library, providing support for a wide range of data formats, and utilize Dask for parallel computation, making them scalable for distributed computation infrastructures. By enabling InSAR data analysis incorporating contextual information, the two libraries enhance the potential to uncover underlying mechanisms driving deformation phenomena.

@en

Point cloud is made up of a multitude of three-dimensional (3D) points with one or more attributes attached. Point cloud is the third data paradigm in addition to the well-established object (vector) and gridded (raster) representations, since point cloud data can be directly collected, computed, stored, and analyzed without converting to other types. Modern ways of data acquisition, including laser scanning from airborne, mobile, or static platforms, multi-beam echo-sounding, and dense image matching from photos, generate millions to trillions of 3D points with attached attributes. If the collection is carried out in different periods, one of the essential attributes is precisely time, allowing spatiotemporal analysis to be performed. Its use is widespread in some fields such as metrology and quality inspection, virtual reality, indoor/outdoor navigation, object detection, vegetation monitoring, building modeling, cultural heritage, and diverse visualization applications. There are some examples in fields related to hydroinformatics, mainly related to terrain modeling. Due to its nature of big data, over the past decades, a series of developments have been carried out in the different processing chains for the optimal use of point cloud. This research seeks to introduce the various point cloud developments from which the hydroinformatics community and research could benefit. A review of recent advances is made, mainly including the analysis and visualization of point cloud for dealing with water-related problems. Potential areas of application and development in hydroinformatics are identified. These include, for example, the topics of coastal monitoring, coastal erosion, shallow water assessment, ice sheet change analysis, sea-level rise assessment, monitoring of levels in water bodies, crop and vegetation monitoring, analysis of the effects of groundwater depletion, detail tracing of basins and channels, analysis of floods with detailed terrain models, and drought monitoring in crops and forests. The challenges to overcome and ongoing developments regarding point cloud application in hydroinformatics are also discussed.@en