Conference Agenda

Understanding vegetation dynamics in three-dimensional, high-temporal resolution is essential for advancing ecological research and sustainable forest management. This study introduces a novel methodology for tracking branch kinematics in trees using hyper-temporal terrestrial laser scanning (TLS) data. Focusing on a solitary pedunculate oak (Quercus robur) over a one-year period, we employed a geometric feature detection algorithm combined with quantitative structure modeling (QSM) to identify and track distinctive point cloud sections on first- and second-order branches. By leveraging an iterative closest point (ICP) alignment process, branch kinematics were analyzed across multiple epochs, yielding detailed three-dimensional movement trajectories. The results demonstrate that branch movements exhibit screw-shaped patterns. Temporal resolution analysis revealed that a one-week recording interval is sufficient for our study subject to reliably capture kinematic dynamics, whereas longer intervals (e.g., three weeks) result in significant deviations from actual trajectories. The proposed method proved robust against partial occlusions from leaf growth but struggled under extensive occlusions. This research highlights the potential of hyper-temporal TLS for non-contact, high-resolution monitoring of tree canopy dynamics and provides a foundational approach for future studies aimed at modeling vegetation movement and structural changes over time.

3D instance segmentation for laser scanning (LiDAR) point clouds remains a challenge in many remote sensing-related domains. Successful solutions typically rely on supervised deep learning and manual annotations, and consequently focus on objects that can be well delineated through visual inspection and manual labeling of point clouds. However, for tasks with more complex and cluttered scenes, like in-field plant phenotyping in agriculture, such approaches are often infeasible. In this study, we tackle such a task - in-field wheat head instance segmentation using terrestrial laser scanning (TLS) point clouds. To address the problem and circumvent the need for manual annotations, we propose a novel two-stage pipeline. To obtain the initial 3D instance proposals, the first stage uses 3D-to-2D multi-view projections, the Grounded SAM pipeline for zero-shot 2D object-centric segmentation, and multi-view label fusion. The second stage uses these initial proposals as noisy pseudo-labels to train a supervised 3D panoptic-style segmentation neural network. Our results demonstrate the feasibility of the proposed approach and show significant performance improvements (up to +50\% in F1-score) relative to Wheat3DGS, a recent alternative solution for in-field wheat head instance segmentation without manual 3D annotations based on multi-view RGB images and Gaussian Splatting, showcasing TLS as a competitive sensing alternative. Moreover, the results show that both stages of the proposed pipeline can deliver usable 3D instance segmentation without manual annotations, indicating promising, low-effort transferability to other comparable TLS-based point cloud segmentation tasks.

Prompted by the rapid advancements in software and hardware, 3D building data for numerous different applications is nowadays often captured via mobile or kinematic laser scanning. However, in contrast to other laser scanning methods, there exist only a few approaches tailored for the planning of a kinematic laser scan survey, and none of them provides an optimality guarantee. Therefore, we propose a novel approach based on Mixed Integer Linear Programming (MILP) to find the optimal trajectory for such a survey. To obtain a high-quality point cloud, we account for scanner-related constraints that influence the quality of the resulting point cloud. Moreover, we enable the introduction of tie points to mitigate the effects of uncertainties in the position estimation that are propagated in the acquired data. In our problem formulation, we aim to find the best tour in a properly weighted graph. For this, we propose two different weight settings to either enable a purely length-based optimization or to increase the redundancy in the measurements by incorporating a Visibility Ratio Factor (VRF) into the objective function.

To prove the applicability of our approach for offline panning, we apply our formulation to three different scenarios. In this context, the VRF-based weighting enables a significant speed-up of the solving process while resulting in only slightly prolonged routes. This approach paves the way for applying exact algorithms with an optimality guarantee in the planning process for efficient kinematic laser scanning surveys.

This contribution introduces a novel method for non-contact, marker-free modal analysis of wind turbine blades using terrestrial laser scanning (TLS). As part of a research initiative, TLS's potential for assessing modal properties like natural frequencies and mode shapes—key for extending blade service life—is explored. Traditionally, this analysis relies on numerous accelerometers, incurring high costs and effort. TLS is evaluated as a viable alternative.

In laboratory tests, TLS and photogrammetry were used on a 4-meter test object in vibration. Photogrammetric data, serving as a reference, used 3D coordinates from retroreflective markers for frequency analysis via Fast Fourier Transform (FFT). TLS data were similarly segmented, with frequencies derived using FFT, and both methods showed consistent results, validating TLS's feasibility.

Building on lab results, the method was applied to an 88-meter rotor blade in a field experiment. The laser scanner collected profile data along the blade's longitudinal axis, converted to the object coordinate system. By segmenting the blade, eigenfrequencies were determined. The calculation process was validated with simulations, achieving precise results even with manual blade excitation and amplitudes up to 20 cm.

TLS measurements reveal valuable insights into eigenfrequencies and modal shapes along the blade. This approach offers a cost-effective, efficient alternative to traditional sensor-based analysis, proving its practicality for the wind energy industry.

Hybrid airborne imaging systems combining oblique cameras and LiDAR sensors offer significant advantages for applications requiring both geometric precision and rich texture information, including infrastructure monitoring, facility surveying, and detailed urban modeling. Despite capturing temporally consistent multi-modal data, achieving pixel-level registration between imagery and LiDAR remains fundamentally challenging due to insufficient calibration infrastructure and the technical complexity of deeply integrating heterogeneous sensors.

A critical bottleneck is that standard photogrammetric workflows exhibit non-linear cumulative drift, particularly across extended flight strips. This spatially varying deformation causes systematic misalignments when photogrammetric reconstructions are overlaid with LiDAR geometry. Conventional approaches applying global rigid transformations fail to address this issue because photogrammetric drift is inherently non-uniform—a single global registration cannot correct localized geometric deviations throughout the scene.

This work introduces a novel view-dependent registration framework that synergizes LiDAR's global geometric fidelity with photogrammetry's local density. Rather than attempting to warp entire models through global transformations, we decompose the registration problem by treating the geometry within each camera frustum as an independent rigid body. Building upon initial georeferencing, we perform fine-grained local SE(3) rigid registration to anchor each Multi-View Stereo (MVS) depth map directly to sparse LiDAR geometry within its corresponding viewing frustum.

This localized approach enables pixel-accurate alignment within individual frames while effectively compensating for accumulated photogrammetric drift and interpolation errors. By addressing registration at the frustum level rather than globally, our method achieves practical pixel-level fusion of hybrid airborne datasets, unlocking the full potential of integrated camera-LiDAR systems for high-precision geospatial applications.

High-resolution Digital Elevation Models (DEMs) are essential for urban flood modelling, where small elevation differences govern surface drainage and inundation extent. DEMs frequently contain hydrological inconsistencies: elevated infrastructure such as bridges, tunnels and culverts may appear as artificial barriers disrupting flow continuity, while linear structures such as retaining walls may be underrepresented depending on spatial resolution or point density. These inconsistencies propagate errors through downstream hydraulic simulations.

This paper presents an automated, open-source Python pipeline for generating hydrologically conditioned DEMs by integrating classified airborne LiDAR data with OpenStreetMap (OSM) infrastructure features. The workflow is tested on a 16 km2 area of central Copenhagen using a 2023 national LiDAR acquisition at 13.5 pts/m2. A 0.5 m resolution DSM is generated from LiDAR ground and building classes via Inverse Distance Weighting interpolation, with Nearest Neighbour gap-filling for hydraulic model continuity. Hydrological conditioning is performed through four sequential operations: bridge burning, tunnel enforcing, culvert enforcing, and barrier rasterization. Barrier top-of-wall elevations are estimated directly from the LiDAR point cloud.

Vertical accuracy is assessed by pixel-wise comparison against the Danish national terrain model DHM/Terraen (NMAD = 0.066 m, LE90 = 0.265 m) and by independent checkpoint validation against the HojdefikspunktDanmark geodetic network. The inclusion of shallow tunnel underpasses proved a significant addition: tunnel features alone contributed approximately half of the total depression volume reduction. The conditioned DSM is designed as input for an urban flood simulation chain; full hydraulic validation will be performed by the Danish Meteorological Institute within the CLEAR-EO project.