Today several methods exist to capture 3D point clouds for geoscientific analysis at a wide range of spatial scales with high-end but also low-cost tools and devices. Most prominent approaches are image-based point cloud generation and active laser scanning (also referred to as LiDAR) operated on various platforms: e.g. hand-held, static on tripods, or kinematic on cars and airborne manned or unmanned vehicles (UAV). The strongly increasing availability of 3D point clouds demands for new methods and their evaluation for direct usage of point clouds in geosciences.
Contributions focusing on 3D point cloud capturing, georeferencing, processing, management, infrastructures, analysis, and visualization in the geosciences are welcome. This includes studies on low-cost sensing (e.g. Kinect, smartphones), image-based point cloud generation (e.g. new SfM and dense image matching approaches) and innovative LiDAR point cloud studies. Additionally, studies dealing with new point cloud features, classification approaches (machine learning), change detection results, and object recognition, detection and modelling methodologies are asked for. Finally, smart data reduction techniques, quality assessment, fusion of different data sources, and GIS workflows using 3D point clouds are in the focus of this session. Particularly, contributions tackling processing and evaluation of multi-temporal 3D point clouds will be promoted.