Conference Agenda

We present the first quantitative evidence of systematic daily dead tree branch movement under field conditions, which has been previously only observed qualitatively. Using a boreal forest Permanent Laser Scanning (PLS) dataset, we investigated whether such movement can be detected and characterized in 3D laser scans. We link branch anatomy and geometry with environmental drivers, leading to the hypothesis that branch bending is proportional to wood moisture content. Hourly point clouds collected over 3.5 days from 17 dead branches (16 coniferous), attached mainly to living trees, were analyzed under calm weather conditions. We developed a novel workflow that tracks movement via non-rigid registration, calculates the angle between each branch and the vertical, and quantifies branch movement uncertainties across epochs. After accounting for time lags, these movements were related to the modeled wood moisture content using a linear mixed model. Clear and consistent daily movement was detected in all branches, with a mean amplitude of 21 cm and an average delay of 3.5 hours relative to moisture content changes. All branches moved downward during the day and upward at night, except one deciduous branch displaying the opposite pattern, consistent with our theoretical framework. The linear mixed model revealed a significant positive linear relationship between branch movement and wood moisture content. Our findings confirm that daily dead tree branch movement is primarily hygroscopic and demonstrate its effective detection using operational PLS. These insights open new possibilities for monitoring tree vitality using hypertemporal 3D sensing.

Tree growth determines how much CO2 is sequestered from the atmosphere and temporarily stored in woody biomass. At the same time tree growth is affected by increasing temperatures, more frequent drought periods, late frosts and other extreme events associated with climate change. While continuous measurements of radial (secondary) tree growth using dendrometers are well established, monitoring of shoot elongation (primary growth) has largely been neglected because suitable measurement techniques are lacking. As a result, the effects of climate change on primary tree growth remain insufficiently understood. This work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies. Here we explored the use of low-cost UAV photogrammetry and of a multi-camera CraneCam system under real-world conditions. Data were collected in two study areas over an entire growing season. We present sensor evaluations, photogrammetric data acquisition and processing strategies. A special focus is placed on the analysis of the resulting photogrammetric 3D point clouds in terms of accuracy, resolution and completeness. Results demonstrate 3D point accuracies of 5-6 mm for entire trees using consumer-grade UAVs weighing less than 250 g and a 3D reconstruction completeness between 92% and 98% depending on the UAV type. The paper introduces a novel 3D-printed ground-truth branch to evaluate the capability to reconstructing fine-detail structures such as thin tree shoots. Finally, we discuss operational challenges and initial experiments towards a skeletonization of entire trees based on photogrammetric point clouds.

Urban trees provide critical cooling benefits and regulate microclimates in cities, yet their three-dimensional thermal behaviour remains difficult to visualize and communicate to stakeholders. Traditional approaches rely on 2D thermal imagery analyzed on desktop screens, lacking spatial context and the capability for temporal monitoring across phenological stages. This work presents a pipeline combining Gaussian Splatting with augmented reality to enable immersive, on-site visualization of urban tree thermal patterns.

We captured thermal infrared (TIR) images of silver linden trees (Tilia tomentosa) at the University of Strasbourg using a FLIR T560 thermal camera. The TIR images were preprocessed and then processed using MILo (Mesh-In-the-Loop Gaussian Splatting), generating 3D thermal meshes with approximately 3 million vertices. The reconstruction is validated against reference thermal point clouds acquired with a terrestrial laser scanner equipped with an integrated thermal camera, assessing both geometric completeness and thermal attribute fidelity.

The thermal meshes are deployed in a mobile augmented reality application, allowing users to visualize temperature distributions directly overlaid on physical trees in the field. This work demonstrates the first application of 3D Gaussian Splatting to thermal vegetation modelling, providing an engaging educational tool to communicate urban trees' cooling role while offering researchers a platform for detailed thermal analysis and forest health monitoring.

To robustly support glacier calving monitoring at high temporal resolution and enable future AI-based calving forecasts, this study presents an optimized Multi-Epoch Multi-Imagery (MEMI) strategy for automated 4D point cloud model generation. To date, the dataset comprises over 160,000 images acquired since December 2024 by an autonomous multi-camera system operating at 30 min intervals at Glacier Perito Moreno (GPM), Argentina. Despite high scene variability and harsh environmental conditions, the proposed MEMI workflow effectively addresses constraints imposed by continuous glacier motion and image degradation. The enhanced strategy aims to generate precise dense clouds with high alignment accuracy and computational efficiency, forming the basis for subsequent analysis of glacier front evolution. To achieve this, various parameter configurations are evaluated, including AI-based image masking and adaptive, optimized alignment-adjustment settings. Results from a representative eight-day subset show that variations in the tie point computation strategy lead to measurable differences in alignment-adjustment efficiency, with the best configuration being about 11 % faster than the least efficient one. By contrast, adaptive alignment-adjustment consistently improves alignment accuracy. Moreover, masking enhances both image quality checking and reconstruction quality, and, albeit modestly, improves pre-failure deformation analysis. Furthermore, daily seasonal responses to alignment are observed, as accuracy varies with solar illumination relative to the camera positions. Applying the optimal configuration to 260 MEMI projects in under 42 h produced 518 high-precision dense clouds and detected calving retreat magnitudes of up to 17.5 m, demonstrating the robustness and scalability of the proposed MEMI strategy for high-temporal-resolution 4D point cloud generation.

Daily satellite-derived nighttime lights (NTL) are increasingly used to monitor electricity disruption and recovery, but their reliability in tropical regions is constrained by persistent cloud cover and intermittent observation. This study adopts a diagnostic-first framework that treats observability as a prerequisite for interpretation, determining when daily NTL signals are sufficiently supported to reflect underlying grid dynamics. Using NASA’s VIIRS Black Marble product, we quantify spatial and temporal completeness and radiance stability across the Samar–Leyte sub-grid in the Philippines. Results show that observations are highly intermittent and spatially heterogeneous, with urban areas providing more stable and interpretable signals, while rural regions remain noise-dominated.

To assess whether reliability-qualified NTL reflects electricity demand, DNB-BRDF radiance is aligned with hourly load data from the National Grid Corporation of the Philippines (NGCP) using settlement-based masks derived from GHSL SMOD. Alignment is evaluated using correlation, error, and retained coverage, combined into a composite score. Strongest agreement occurs under low to mid-range valid-pixel thresholds and within urban-focused masks, which balance signal fidelity and temporal continuity at the cost of reduced coverage. Replication across four additional Visayas sub-grids shows that optimal threshold–mask configurations vary by region, reflecting differences in cloud regime and settlement structure.

These results establish explicit conditions under which daily NTL can be interpreted as a proxy for grid dynamics. The framework provides a reproducible basis for reliability-qualified analysis using globally available datasets and can be tested in other cloud-prone regions where ground-based data are limited.

The maintenance of airport pavements is critical to ensuring the safety and efficiency of air operations. Conventional inspection methods are often time-consuming, subjective, and prone to inconsistencies in data collection. Recent advances in unmanned aerial vehicle (UAV) photogrammetry offer a potential alternative for improving inspection efficiency and measurement accuracy.

This study evaluates the applicability of UAV-based photogrammetry for the detection and quantification of pavement distresses under conditions representative of airport infrastructure. Image data were acquired at different flight altitudes and overlap configurations and processed using Structure-from-Motion techniques to generate high-resolution orthomosaics and Digital Elevation Models (DEMs). The resulting datasets were analyzed to identify, delineate, and classify deterioration types and severity levels.

The results indicate that a flight altitude of 10 m combined with 80% longitudinal and 70% transversal overlap provides an optimal balance between spatial resolution and operational efficiency. Under unobstructed conditions, photogrammetric analysis detected more than 98% of existing distresses and enabled more precise geometric delineation compared to traditional field-based methods. Undetected distresses were primarily associated with shadowed or obstructed areas, highlighting the influence of environmental conditions on detection performance.

Overall, the findings demonstrate that UAV-based photogrammetry is a reliable and efficient approach for pavement condition assessment, with significant potential to enhance data quality and reduce inspection time in airport infrastructure management.

3D change detection is essential for monitoring infrastructure, environmental dynamics, and natural hazards. However, existing algorithms are often evaluated on single-scene datasets, and their generalization across varied real-world scenes remains largely unexplored due to the absence of a universal benchmark. To address this issue, we propose MultiChange3D, a multi-scene, multi-sensor 3D change detection dataset for identifying geometric changes in 3D space. The dataset provides registered pairs of point clouds with ground-truth geometric change labels, enabling standardized evaluation across different methods. To demonstrate the use of the MultiChange3D dataset, we benchmark an initial set of approaches on a subset of the dataset. The evaluated methods include classical Euclidean distance-based methods (C2C, M3C2), 3D displacement estimation-based approaches (F2S3, Landslide-3D), and deep learning-based classification methods (KPConv, EF-KPConv, PGN3DCD). Quantitative and qualitative analyses indicate the strengths and limitations of the evaluated methods, highlighting the challenges in cross-scene generalization under variations in point density, scale, and types of changes. The full dataset and evaluation code is openly available at: https://github.com/3DOM-FBK/multichange3d.

Quantifying topographic dynamics from 3D point cloud time series is essential for geoscientific applications. However, laser scanning data typically varies between epochs in point density due to differing survey properties. These irregularities present a challenge for change detection, particularly across multi-temporal and multi-modal data. We propose a new approach, adaptive temporal aggregation, as an extension of the Multiscale Model to Model Cloud Comparison (M3C2) algorithm. Driven by a local point density requirement, our method employs both a spatial and a temporal neighborhood. If a core point's neighborhood is too sparse for M3C2 estimation, an iterative temporal search progressively incorporates data from temporally adjacent epochs until the density requirement is met or a maximum temporal window is reached. This adaptive process ensures sufficient local density while preventing unnecessary temporal aggregation, a key advantage over global aggregation. We evaluated our method on a multi-modal dataset from the Mont-Saint-Michel Bay, France (38 irregular epochs, ~1 decade). Results demonstrate significantly improved change detection, increasing completeness by >13% (vs. standard M3C2) and accuracy by 31% (vs. fixed-window averaging). Our work provides a robust approach for enhancing 3D change detection algorithms for complex, real-world 4D datasets, enabling higher accuracy and completeness in analysing surface dynamics.