The accuracy of vertical position information can be degraded by various sources of error in digital aerial photogrammetry (DAP) based point clouds. To address this issue, we propose a relatively s…
After more than twenty years of commercial use, laser scanners have reached technical maturity and consequently became a standard tool for 3D-data acquisition across various fields of application. …
Supervised deep learning algorithms have recently achieved state-of-the-art performance in the classification, segmentation and analysis of 3D LiDAR point cloud data in a wide-range of applications…
Real-time semantic segmentation of point clouds has increasing importance in applications related to 3D city modelling and mapping, automated inventory of forests, autonomous driving and mobile rob…
In the view of climate change, understanding and managing effects on coastal areas and adjacent cities is essential. Permanent Laser Scanning (PLS) is a successful technique to not only observe not…
Fine-grained information on the level of individual trees constitute key components for forest observation enabling forest management practices tackling the effects of climate change and the loss o…
Tree species characterise biodiversity, health, economic potential, and resilience of an ecosystem, for example. Tree species classification based on remote sensing data, however, is known to be a …
First order geometric network design is an important quality assurance process for terrestrial laser scanning of complex built environments for the construction of digital as-built models. A key de…
Three-dimensional (3D) point cloud registration is a fundamental step for many 3D modeling and mapping applications. Existing approaches are highly disparate in the data source, scene complexity, a…
Three-dimensional (3D) point cloud registration is a fundamental step for many 3D modeling and mapping applications. Existing approaches are highly disparate in the data source, scene complexity, a…