Conference Agenda

Smoothing aerial LiDAR point clouds is challenging, because they are often noisy, irregularly sampled, and sparse, as well as their inherent high degrees of freedom. Classic methods struggle on such datasets as they were designed for regularly sampled, dense datasets with moderate noise. To address the challenge, this paper proposes a constrained point cloud model with one degree of freedom. The point cloud model incorporates the sensing directions stored in the full waveform LiDAR datasets, and has theoretical advantages in terms of the statistical error bound for normal estimation. Based on the point cloud model, the directional total least square is formulated as a regularized convex optimization problem for points estimation on a tangent plane. Moreover, a non-convex regularizer along with the non-convex regularized directional total least square is proposed to improve the estimation

quality. To solve the proposed optimization problems, an accelerated Douglas-Rachford splitting algorithm is introduced. The proposed methods demonstrate better performances on simulated two-dimensional point clouds in terms of improved root-mean square-

error. For three-dimensional aerial LiDAR point clouds, implemented under the Savitzky-Golay filter framework with local

smoothness prior, the proposed methods demonstrate more smoothing power and robustness than the classic method.

Urban mapping for planning and monitoring requires high-resolution spatial data, especially in areas with high landcover diversity. Airborne LiDAR Scanning (ALS) provides accurate 3D point cloud data, but its classification remains challenging due to computational complexity, irregular point distribution, noise, mislabeling and outliers in the dataset. These challenges are amplified in dense urban environments with mixed vegetation and infrastructure. Existing local context-based classification methods consider all points equally, overlooking the impact of their spatial position of the point in the dataset. To address this, we propose a dynamic local context-based point confidence-based optimization that improves classification accuracy by leveraging the spatial context of each point. This approach selects points based on confidence levels derived from position indices in training data and predicted by binary classifiers in test data to enhance robustness of classifier. We evaluated the proposed approach using boosting-based machine learning classifiers on two datasets: Thiruvananthapuram Aerial LiDAR Dataset (TALD) from India and the ISPRS 3D semantic labeling dataset from Vaihingen, Germany. The results showed 90.3% accuracy on TALD and 90.0% on Vaihingen, achieving a 2-4% improvement over conventional local context-based classification.

Accurate interpretation and segmentation of 3D point clouds in real-world urban environments is a critical challenge in geospatial analysis, particularly due to the complexity of real-world scenes, inevitable data uncertainties, and potential annotation errors. This paper proposes a confidence-aware deep learning framework to refine the segmentation accuracy of real-world point cloud data. By incorporating multi-source information, such as aerial imagery, and embedding geospatial prior knowledge, this framework models data uncertainty through point-wise confidence scores. Besides, we design an iterative online learning strategy, allowing the network to improve both its predictions and the quality of training labels. Extensive experiments on large-scale airborne laser-scanned data demonstrate that our framework effectively enhances training data by reducing label noise and improving annotation quality, which leads to more robust, generalizable model performance. Our source code is publicly available at https://github.com/AutumnMoon00/RefineNet.

LiDAR sensors incorporated within modern smartphone and tablet devices enable relatively quick and inexpensive collection of ground-based LiDAR data applicable for ground truth mapping as needed for modelling understory vegetation. However, this LiDAR data often requires conversion and processing prior to research use. This study presents a workflow with algorithms utilizing structured query language (SQL) to efficiently process detailed rasterized features from LiDAR data collected by an iPhone Pro Max via the ForestScanner app. After transformation of the LiDAR data, SQL has been employed to voxelize the LiDAR data from which rasterized features have been derived. Various cell sizes for voxels and subsequent pixels have been investigated, leading to a recommended spatial resolution of 0.05 m for cell size dimension. SQL provides precise control for advanced querying to process ground-based LiDAR data for vegetational modelling applications.

In the era of information revolution, with data being the fuel of AI and analytics, efficient information extraction from LiDAR point clouds becomes indispensable for solving real-world problems and aiding decision-making in geospatial domain. Despite having geometric richness, the massive LiDAR point clouds are not only computationally demanding but also lack inherent semantics. The lack of semantics in LiDAR constrains effective data analysis. This paper presents a novel workflow by incorporating Deep Learning derived embeddings as attributes in the geospatial database for the spatio-semantic querying on Aerial LiDAR point clouds. This work leverages AI-based indexing, such as IVFFlat(Inverted File Index with Flat Quantization) on LiDAR point clouds for fast retrieval of queries. The pgPointCloud and pgVector extensions of PostgreSQL aid in importing point clouds into the database and performing similarity-based query retrieval on the embedding space of the point clouds. The methodology developed in this paper explores how semantic embeddings can handle inadequate semantics of point clouds by enabling direct and complex 3D intelligent queries within the database environment, thereby overcoming the limitations of traditional LiDAR representations. Few queries presented in this paper highlight the applications of this proposed framework in individual tree detection, tree species identification, utility management, urban planning and anomaly detection.

The development of global geographic information resource products involves massive information processing of PB-level multimodal spatiotemporal data, and faces technical challenges brought by the global scale. In response to the challenges, we have made technological innovations to break through the key technologies for the development of global geographic information data products. With the main themes of "intelligent interpretation of typical elements, multi-source geographic data mining, and intelligent hybrid compilation", we have conducted and completed the overall technical research on the construction of global geographic information resources, formed an autonomous construction capability. Firstly, through crowd-sourced data mining and fusion technology to achieve content information extraction and knowledge fusion; Secondly, using multiple source data features, fast automatic extraction and integration of elements based on deep learning models was processed, and produce digital line graph data based on intelligent hybrid compilation. Based on the automatic feature extraction technology of deep learning, the production of digital line graph data products has been updated, and the accuracy evaluation has reached over 85%.

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.

As an essential component in the atmosphere, black carbon (BC) can affect regional and global climate, air quality, and human health. Biomass burning is an important source of BC aerosols, and biomass burning in East Asia is rather active. In this study, we analyze the biomass burning over East Asia in 2010 using MODIS satellite fire data. A global chemical transport model, GEOS-Chem, is used to simulate temporal and spatial variations of BC aerosols and impact of biomass burning on these variations through two numerical experiments: one with all BC emissions while the other without the biomass burning emissions.

The results show that the 2010 biomass burning over East Asia frequently occurred in northeast China, north China, northern India and indo-China Peninsula. In China, biomass burning mostly happened in summer and fall, while in Southeast Asia, biomass burning happened in spring and winter. GEOS-Chem can reasonably reproduce the temporal and spatial variations of BC. The surface concentrations of BC in China are high in the North China and Southwest basins. Such a spatial pattern is similar in four seasons, with seasonality that BC concentrations are the highest in winter, followed by autumn, spring and summer. Sensitivity analysis shows that the biomass burning in East Asia contributed 8.6% BC concentrations in East Asia. Based on the EOF decomposition and correlation analysis, the BC concentrations due to biomass burning in some parts of East Asia was significantly increased through transport of BC in the first mode at 850 hPa in spring and winter.

The Chinese second-generation Directional Polarization Camera (DPC-2) onboard the GF-5(02) satellite provides global multi-angle, multispectral polarization observations, effectively bridging the gap between POLDER/PARASOL and SPEXone/PACE. Using one year of DPC-2/GF-5(02) measurements, land-ocean aerosol products are generated by fully exploiting polarization and angular information to enhance sensitivity to aerosol properties. Ground-based observations from the AErosol RObotic NETwork (AERONET) are used to evaluate the retrieval accuracy of Aerosol Optical Depth at 550 nm (AOD550), Ångström Exponent between 440 nm and 670 nm (AE440-670), and Single Scattering Albedo at 440 nm (SSA440), demonstrating the stability and reliability of the retrievals. For AOD550, the Root Mean Square Error (RMSE) and bias are 0.109 and -0.006 over land, and 0.071 and -0.001 over ocean. For AE440-670, the RMSE and bias are 0.488 and -0.151 over land, and 0.275 and -0.047 over ocean. For SSA440, the RMSE and bias are 0.044 and 0.003 over land, and 0.039 and 0.002 over ocean. Comparisons with mainstream satellite aerosol products indicate comparable and consistent accuracy. Overall, these results provide a coherent global characterization of aerosol distribution and properties, highlighting the strong potential of DPC-2/GF-5(02) for long-term aerosol monitoring and climate research.

This study involves remote sensing and Lidar-based data analysis to quantify the aerosol extinction profile under different urban patterns and seasons.

Direct and indirect climate forcing from Asian dust storms has been well documented, such as lifted dust aerosols becoming cloud-forming particles and changing radiation flux from surface to the top of atmosphere. However, whether such forcing becomes distinguished as related to dust origins remains unclear. Here we present a comparative analysis of historical dust storms in China originating in Mongolia and Xinjiang from 2016-2023, and determine their respective dominators by involving their individual and combined influence on dust storms. Most dust storms originated in Mongolia, with observed long-range transport and global scale development, in comparison to those originating in Xinjiang. During dust storms, cloud properties such as cloud droplet radius and cloud retrieval fraction liquid had nonlinear response, and a dominant role in 60.2% of the study area. Climate conditions such as surface thermal radiation and dewpoint temperature became dominated in periphery of dust storms. Xinjiang-originated dust storms, in contrast, were dominated by local aridity (65.2%). As the aridity decreased, dust storms were dominated by total precipitation, with increase from 0.5 up to 3.6, and the influence of surface heat flux decreased. Heat-flux-dominated regions encountered increased aridity, and the dominance of total precipitation was neutralized. These findings have important implication for global management and mitigation of Asian dust emissions.

The Arctic Observing Mission (AOM) is a satellite mission concept under study by the Canadian Space Agency (CSA), in partnership with Environment and Climate Change Canada (ECCC) and Natural Resources Canada (NRCan). AOM would use two satellites in a highly elliptical orbit (HEO) to enable frequent observations of meteorological variables, greenhouse gases (GHGs), space weather and air quality (AQ) over northern regions, reaching beyond the usable viewing range of geostationary satellites. These observations are important for operational activities, environmental monitoring and scientific research aligned with the Government of Canada priority of enhancing Arctic and northern situational awareness and security.

This study presents a hyperspectral remote sensing approach for monitoring global CO₂ point source emissions using China’s GF5 and ZY1 satellites. By applying the matched filter method in the 1.6 μm and 2.0 μm absorption band and the Integrated Mass Enhancement (IME) technique, this study successfully detects and quantifies emissions from multiple facilities within a single scene—demonstrated in a high-density industrial cluster in Xinjiang. Results show current systems can detect power plants with annual emissions above 2.90 MtCO₂, covering 6.74 GtCO₂/year globally across eight sectors. While power and chemical sectors are well captured, cement and petrochemical emissions remain poorly detected, highlighting the need for improved sensitivity to low-intensity sources.

Biomass burning releases a large amount of pollutants including carbon monoxide (CO), and generates secondary pollutants, e.g., ozone (O3). Both CO and O3 are major pollutants and can also significantly affect tropospheric chemistry. Understanding O3-CO relationship is important for evaluating transport and evolution of the pollutants in fire plumes. Here, we analyse the satellite remote sensing of fire count data from MODIS, satellite remote sensing of CO and O3 from AIRS, and the simulation of the global atmospheric chemistry model GEOS-Chem in the middle and lower troposphere during June and August of 2010. AIRS can capture fire-induced CO and O3 enhancements (ΔCO and ΔO3) well in fire-affected and fire-plume outflow regions. Two areas with high ΔCO and ΔO3 include central Africa and northwestern South America in the tropics, where the numbers of hotspots are the large in the MODIS fire data. AIRS CO and O3 in fire plumes are highly correlated in 850 hPa and 500 hPa. The GEOS-Chem simulation show CO and O3 enhancement in northwestern South America, but with lower ΔO3/ΔCO values. These findings highlight the importance of integrating satellite observations with atmospheric chemistry modelling on refining fire-affected air quality and tropospheric chemistry assessments.