Editor(s)Döllner, Jürgen and Jobst, Markus and Schmitz, Peter
AbstractToday, landscapes, cities, and infrastructure networks are commonly captured at regular intervals using LiDAR or image-based remote sensing technologies. The resulting point clouds, representing digital snapshots of the reality, are used for a growing number of applications, such as urban development, environmental monitoring, and disaster management. Multi-temporal point clouds, i.e., 4D point clouds, result from scanning the same site at different points in time and open up new ways to automate common geoinformation management workflows, e.g., updating and maintaining existing geodata such as models of terrain, infrastructure, building, and vegetation. However, existing GIS are often limited by processing strategies and storage capabilities that generally do not scale for massive point clouds containing several terabytes of data. We demonstrate and discuss techniques to manage, process, analyze, and provide large-scale, distributed 4D point clouds. All techniques have been implemented in a system that follows service-oriented design principles, thus, maximizing its interoperability and allowing for a seamless integration into existing workflows and systems. A modular service-oriented processing pipeline is presented that uses out-of-core and GPU-based processing approaches to efficiently handle massive 4D point clouds and to reduce processing times significantly. With respect to the provision of analysis results, we present web-based visualization techniques that apply real-time rendering algorithms and suitable interaction metaphors. Hence, users can explore, inspect, and analyze arbitrary large and dense point clouds. The approach is evaluated based on several real-world applications and datasets featuring different densities and characteristics. Results show that it enables the management, processing, analysis, and distribution of massive 4D point clouds as required by a growing number of applications and systems.