Conference Agenda

LoD-2 building models are more informative and practically more useful than LoD-1 representations because they capture the roof structure that defines the essential three-dimensional form of a building. They are important for applications such as urban planning, environmental simulation, and digital heritage. Although recent roof shape extraction methods can derive vectorised 2D roof structures from very-high-resolution imagery, transforming these image-based representations into fully textured 3D buildings remains challenging. In this paper, we present a semi-automated LoD-2 reconstruction pipeline that integrates HEAT-derived roof geometry with airborne LiDAR, satellite and Google Street View imagery. The 2D outputs are reprojected into map coordinates, fused with LiDAR through a two-stage roof reconstruction strategy to derive roof shapes and combined with an adaptive, LiDAR-based ground base initialisation to create a complete 3D wireframe. Roofs are textured using VHR orthophotos while the walls are textured via a process of Street View panorama selection, geometric filtering, Mask2Former segmentation, and homography rectification. Across a large-scale evaluation on 1000 buildings, the proposed two-stage reconstruction strategy improves geometric agreement with the LiDAR reference data achieving a roof-surface RMSE of 0.445~m. The wall texturing process produces convincing facades when suitable panoramas are available. While minor challenges such as sensitivities to LiDAR outliers, incomplete roof geometry, and facade occlusions persist, this pipeline effectively bridges 2D roof parsing and textured LoD-2 model generation, providing a robust and scalable foundation for advancing toward fully automated workflows.

This paper presents an automated framework for generating semantically labelled building point clouds from their corresponding BIM models. The proposed methodology aims to facilitate the creation of training datasets for deep learning–based indoor semantic segmentation. Two complementary labelling strategies are introduced. The first relies on bounding boxes (BBX) extracted from BIMelements to efficiently assign labels to points based on volumetric inclusion. The second approach uses solid geometry and a nearest-neighbour principle (SG-NN) to compute distances between BIM object meshes and the point cloud, enabling a more precise spatial correspondence. In addition, a room-based geometric grouping strategy is proposed to structure the annotated point clouds into spatial units compatible with common indoor segmentation datasets. The methods are evaluated through a qualitative analysis on several real building datasets of different typologies and acquisition conditions, as well as through a quantitative evaluation based on a manually segmented reference point cloud. Results show that the SG-NN approach achieves higher performance, with an average Recall of 92% and IoU of 88%, compared to 87% of Recall and %78 of IoU for the BBX approach. While the BBX approach provides faster processing, the SG-NN strategy achieves higher labelling accuracy, particularly for geometrically complex elements. The proposed workflow enables scalable dataset generation from Scan-to-BIM projects while significantly reducing manual annotation effort.

This paper presents an enhanced building extraction framework that combines deep learning-based image segmentation with photogrammetric point cloud refinement for urban roof detection. The method first applies a modified SegNet model to orthophotos from the ISPRS Vaihingen dataset to generate initial building masks. These results are then refined using geometric information from point clouds through ground filtering, clustering, and normal-guided region growing. By integrating spectral information from imagery with structural cues from 3D data, the proposed framework improves roof boundary delineation and reduces spurious detections. Experimental results on Areas 35 and 37 show that the method achieves strong overall performance, with a precision of 0.96, recall of 0.81, IoU of 0.78, and F1-score of 0.88. The findings indicate that point cloud refinement helps produce cleaner and more reliable building objects than image-based segmentation alone, especially in complex urban scenes. However, the approach remains sensitive to the density and quality of the point cloud. Overall, the study demonstrates that fusing orthophoto segmentation with point cloud processing is an effective strategy for more accurate and geometrically consistent building extraction.

The rapid expansion of urban areas and the continuous demand for their monitoring make remote sensing data a highly valuable tool for collecting large volumes of geospatial information in a relatively short time and with high repeatability. The main objective of this paper is to examine the potential offered by different types of geospatial data, as well as the relationships based on their scope, in comparison with measured reference data.

Architectural inventory tasks are useful not only for engineering projects but also for broader applications, such as environmental impact assessments, spatial planning, and related fields. This article introduces a rapid and cost-effective mixed-mode data collection framework for building inventory development, integrating terrestrial laser scanning, UAV imagery, and traditional ground measurements.

The paper will discuss the latest measurement technologies and their practical applications in building surveying, illustrated with a selected case study. The criteria for selecting appropriate measurement methods will also be analyzed, depending on the investor’s requirements and the intended use of the documentation.

This paper presents a set of techniques for updating the geometric information of buildings using laser scanning and imagery. It begins with an introduction to the fundamental concepts, terminology, and principles of 3D information. Subsequently, various measurement techniques are described, along with a discussion of potential sources of error and data incompleteness. The extracted geometric values are validated against independent survey data.

Accurate 3D reconstruction of urban scenes from multi-view images is essential for city planning, digital twins, and autonomous navigation. Traditional dense image matching relies on low-level cues such as intensity or gradients, which often produce noisy or incomplete point clouds in complex urban environments. This study introduces an attribute-enriched dense matching framework that embeds both geometric features and semantic attributes from multi-view images to guide dense image matching.

The framework first extracts semantic labels and geometric feature correspondences to generate intermediate products: conjugate features, feature seeds, an attribute map, and an initialized disparity map. These elements provide reliable priors that constrain dense matching, reduce search ranges, and prevent mismatches across structural boundaries. Dense image matching then propagates these constraints, producing an attribute-enriched disparity map and point cloud in which each 3D point carries both geometric and semantic information.

Evaluated on urban datasets, the proposed approach improves corner and edge localization, enhances edge continuity, reduces outliers in low-texture areas, and preserves semantic and structural attributes throughout 3D scene reconstruction. By integrating feature-based initialization with attribute-enriched dense image matching, the method delivers more accurate, interpretable, and robust 3D urban reconstructions, supporting downstream tasks such as precise measurement, object recognition, and scene analysis.

The rapid acquisition of high-precision parametric railway alignment is a fundamental prerequisite for intelligent railway construction and maintenance. Traditional measurement techniques and alignment fitting methods heavily rely on manual operations, often resulting in inefficiency, high costs, and insufficient accuracy control. To address these challenges, this study proposes an automated method for extracting and optimizing railway alignment from UAV-based LiDAR point clouds. Initially, track centerlines are extracted by leveraging the geometric smoothness of the railway and the structural characteristics of the track. A multi-constraint energy model integrating distance, orientation, and curvature is constructed to fit the geometric parameters of alignment elements, thereby providing high-quality initial values for subsequent alignment engineering parameter optimization. Finally, a global optimization strategy based on the simulated annealing algorithm is applied to jointly refine the engineering parameters of the standardized alignment composition, ensuring strict compliance with railway design specification. Experimental results demonstrate that the proposed method can efficiently and robustly extract high-precision alignment parameters with well-defined engineering semantics from complex railway point clouds, thereby providing reliable technical support for intelligent construction and full lifecycle management of railway systems.

Semantic segmentation of multi-modal remote sensing imagery plays a pivotal role in land use/land cover (LULC) mapping, environmental monitoring, and precision earth observation. Current multi-modal approaches mainly focus on integrating complementary visual modalities (e.g., optical and synthetic aperture radar (SAR) imagery), yet neglect the incorporating of non-visual textual data a rich source of knowledge that can bridge semantic gaps between visual patterns and real-world concepts. To address this limitation, we propose TSMNet, a text supervised multi-modal open vocabulary semantic segmentation network that synergistically integrates textual supervision with visual representation for open-vocabulary semantic segmentation. Unlike conventional multi-modal segmentation frameworks, TSMNet introduces a dual-branch text encoder to extract both scene-level semantic and object-level label information from various textual data, enabling dynamic cross-modal fusion. These text-derived features dynamically interact with visual embeddings through the proposed text-guided visual semantic fusion module, enabling domain-aware feature refinement and human-interpretable decision-making. Moreover, integrating text opens pathways for open-vocabulary semantic segmentation, enabling systems to recognize and classify unseen categories through natural language descriptions, thereby overcoming the rigid constraints of predefined class taxonomies. To verify our method, we innovatively construct two new multi-modal datasets, and do a lot of extensive experiments are carried out to make a comprehensive comparison between the proposed method and other state-of-the-art (SOTA) semantic segmentation models. Results demonstrate that TSMNet achieves superior segmentation accuracy while exhibiting robust generalization capabilities across diverse geographical and sensor-specific scenarios. This work establishes a new paradigm for explainable remote sensing analysis, demonstrating that textual knowledge integration significantly enhances model generalizability.

Zero-shot visual recognition with vision-language models (VLMs) has shown strong generalization to unseen categories in natural-image benchmarks, yet its effectiveness in remote-sensing (RS) imagery remains less explored. In this paper, we investigate whether meta-prompting with large language models (LLMs) can improve zero-shot scene classification in RS by automatically generating semantically rich class descriptions. Building on the Meta-Prompting for Visual Recognition (MPVR) framework, we evaluate three open-source LLMs, Mixtral-8x7B, Qwen 2.5 7B, and LLaMA 3.1 8B, as prompt generators across five RS benchmark datasets. The resulting descriptions are encoded with several VLMs, including CLIP, MetaCLIP, RemoteCLIP, and CLIP-LAION-RS, and compared against generic single-template and handcrafted domain-specific prompting baselines. Our results show that LLM-generated prompts are competitive with, and in several cases improve upon, manually designed templates, while revealing that the gains depend on both the dataset and the visual backbone. Overall, the study highlights the potential of open-source LLMs as scalable prompt generators for zero-shot remote-sensing recognition and provides insight into the transferability of meta-prompting beyond natural-image domains.

Zero-shot semantic segmentation (ZSSS) is a crucial task in remote sensing image understanding, yet existing methods still suffer from limited generalization to unseen classes. To address this issue, we propose a Knowledge Graph (KG) enhanced ZSSS framework, which introduces explicit hierarchical and relational information into class embeddings to achieve more structured and semantically consistent representations. Specifically, a KG class encoder is designed, consisting of the class enhanced query (CEQ) and class enhanced embedding (CEE) modules, which extract class-relevant subgraphs from a self-constructing Remote Sensing Semantic Class Knowledge Graph (RSSCKG) and generate knowledge-enriched embeddings through a text encoder. Experiments on three public remote sensing datasets demonstrate that the proposed method consistently improves performance across seven state-of-the-art ZSSS frameworks. The integration of KG-based embeddings yields significant gains in the evaluation metrics, with particularly strong improvements on unseen classes, while maintaining accuracy on seen classes. Compared with enhancement strategies based on large language model (LLM) generated descriptions, the proposed KG class encoder exhibit superior semantic separability and stability. These results validate the effectiveness, generalization, and scalability of the proposed framework for ZSSS in remote sensing imagery.

In the era of explainable AI, rapid data processing, analysis, and generation have become essential. Over the past few years, many approaches have been developed to process such heavy data and present it in an explainable manner, including in the field of remote sensing. One of such applications is remote sensing scene description. Many established workflows and models exist, but these models either fail to incorporate essential geospatial information or suffer from hallucination. We present a hybrid multimodal captioning methodology that tightly couples semantic segmentation outputs (via a LoRA-adapted Segment Anything Model) with a small, high-quality LLM- Mistral to produce descriptive, interpretable, and data-grounded scene captions. Rather than relying on direct image-to-text pipelines, our approach first extracts structured scene statistics (class proportions), spatial context (quadrant dominance and object localization), and color fingerprints (dominant colors per semantic class). These structured signals are converted into compact, factual prompts that the LLM consumes to generate coherent, informative, and verifiable captions. A comparison with the established Florence-2 model in terms of quantitative description demonstrates a significant improvement, with the Precision Vocabulary Index increasing from 0.077 to 0.232 due to the proposed workflow.

Ground based time lapse cameras provide continuous, high frequency observations of glacier dynamics; however, automated analysis of these image streams remains challenging due to fog, snowfall, lens contamination, and variable illumination. This study investigates the potential of adapting the foundation segmentation model Segment Anything Model 2 (SAM2) for glacier segmentation from ground-based monitoring. To enable integration into automated pipelines, SAM2 is configured in image mode with a learned prompt generation strategy, while fine-tuning is restricted to the prompt encoder and mask decoder. In addition, the internal Intersection over Union (IoU) prediction head is utilized as a confidence estimator to assess segmentation reliability. Experimental results demonstrate that the adapted model achieves stable segmentation under moderate environmental variability, while degrading under severe visibility loss. This stability is consistent across model scales and input resolutions. The confidence estimation further provides a meaningful signal for identifying uncertain predictions, supporting reliability-aware processing in downstream workflows.

Autonomous train perception must identify the train’s valid path under complex railway geometry, particularly at merging and diverging switches where multiple candidate tracks coexist. Existing approaches are primarily trained as purely visual predictors and typically do not provide justification for route selection, despite the fact that valid paths depend on structured cues such as blade–stock contact, rail gaps, and track continuity. In this work, we adapt the Large Language Instructed Segmentation Assistant (LISA) to railway ego-path perception and formulate the task as reasoning-guided segmentation: given a forward-facing railway image and a natural-language query, the model predicts the valid ego-path mask and, when prompted, generates a textual explanation grounded in visible switch geometry. Our approach integrates railway-specific prompting, a tailored annotation scheme, and efficient finetuning, along with semantic segmentation supervision to support general scene understanding. Experiments on a RailSem19-based evaluation set show improved ego-path segmentation performance over the original LISA checkpoint and increased robustness to prompt variation, while qualitative results indicate that the model can produce plausible, though not always consistent, reasoning. Notably, these capabilities emerge despite the reasoning-specific dataset consisting of only 54 samples, highlighting the data efficiency of the approach. These results highlight the potential of vision-language models for more interpretable railway perception, while also underscoring the need for stronger supervision and evaluation in safety-critical settings. Code and reasoning segmentation data are available at https://github.com/mvakili96/Railway_Perception_FoundationModel.

Terrestrial laser scanning (TLS) enables dense spatial sampling; however, millimeter-level deformation analysis is limited by uncertainty rather than resolution, as inter-epoch differences can arise from actual change or residual systematic effects. Classical methods capture random variability under distributional assumptions but do not guarantee bounds for persistent systematic effects.

This paper presents a complementary interval-based framework that provides reliable, distribution-free bounds for TLS uncertainty and integrates seamlessly with least-squares workflows.

Starting from a measurement and instrumental correction model for high-end panoramic scanners, deviations of effective parameters are propagated to TLS observations and represented as interval radii at the observation level. We then extended the Least-Squares Adjustment, which linearly maps observation-level interval bounds to residuals and parameter estimates, providing conservative first-order enclosures alongside stochastic covariances.

Validation without a trusted nominal is addressed via a residual-based strategy that exploits two-face (Face 1/Face 2) acquisitions. This paper proposes a framework to validate intervals without existing nominal values. It begins with challenges and also guides addressing these challenges to ensure fair validation and test the proposed method on real TLS data. Overall, the proposed framework provides guaranteed bounds for remaining effects, improves discrimination between actual deformation and systematic effects, and offers actionable diagnostics for TLS-based monitoring.

The safety and longevity of civil infrastructure rely on robust structural health monitoring (SHM), yet conventional methods are

often constrained by the high cost and impracticality of contact-based sensors. On the other hand, existing non-contact technologies

typically specialize in either static geometric mapping or spatially limited dynamic vibration analysis, leading to fragmented data

and complex post-processing. This research presents a unified non-contact methodology that addresses this challenge by simul-

taneously acquiring high-resolution 3D geometry time-series vibrational data using a single Light Detection and Ranging (LiDAR)

device. For this purpose, we compare point-based measurements using a total station, an iPhone along with a profile-based LiDAR

and 3D LiDAR point clouds for an experimental analysis. Sensor observations are recorded and analyzed at the same location

on the experimental surface showing flexibility in input dimensionality as well as robustness in resulting scalograms. The core

of the analysis is our developed method, a directional wavelet transform, a signal processing technique uniquely suited handling

non-stationary signals as multidimensional unstructured data. This method enables the characterization of oscillations across the

unstructured 3D surface, a capability beyond traditional modal analysis with one-dimensional time-frequency localization, but using

LiDAR point cloud time series. The result is a richer and more integrated understanding of structural behavior, capable of revealing

vibration behavior in high spatial detail. The study demonstrates that spatio-temporal LiDAR data contains embedded dynamic

information, offering a more comprehensive and efficient way to assess the health and integrity of a structure in the future.

The quasi-continuous capturing of our environment by terrestrial laser scanning (TLS) in form of 3D point clouds provides the basis for numerous spatial analyses, including space-continuous deformation analysis. In times of aging infrastructure and climate

change-induced, cumulative mass movements, statistically-sound methods for determining areal deformations are becoming increasingly important. However, the lack of reproducibility of absolute point positions between consecutive scans and the resence of measurement noise demand approaches that retrieve credible comparison statements. The representation of point clouds by geometric surfaces supports noise reduction and serves as basis for successive analysis. Tensor-product B-spline surfaces have proven to be particularly versatile geometric representations to derive spatially consistent deformation estimates. This paper extends this concept by investigating the use of truncated hierarchical B-splines for statistically sound deformation analysis. We show that deformation is detectable when partition of unity is preserved through truncation. In a simulated environment, significant deformations between two point clouds were successfully detected. Results indicate that coarse surface representations lead to type-1 errors and underestimated deformation magnitudes, whereas more refined surface representations yield consistent deformation estimates, providing a potential termination criterion for adaptive model refinement.

Accurate and spatially dense 3D displacement estimation can contribute to a better understanding of geomorphological processes, while long-range terrestrial laser scanning (LR-TLS) has emerged as a promising technique for generating such observations. However, selecting the most effective algorithms for dense 3D displacement estimation remains challenging due to the lack of benchmarking. This study introduces an open and extensible benchmarking framework for 3D displacement estimation and provides an initial validation through a systematic comparison of representative 2D projection-based and 3D point cloud--based methods for estimating 3D displacements from LR-TLS scans. The evaluation includes 252 combinations of algorithmic and hyperparameter configurations, covering cross-correlation, optical flow, and salient feature tracking approaches, as well as the 3D displacement estimation method F2S3. All methods were benchmarked on a single common LR-TLS dataset, using sparse GNSS and manually derived displacements as ground truth. Results show that F2S3 achieves the highest agreement with the ground truth, while the top-performing configurations of the 2D approaches reach comparable accuracy, albeit slightly lower than that of F2S3. Our findings further highlight key sensitivities of current methods to parameter choices and data characteristics. The presented open and extensible evaluation framework enables reproducible performance assessment and could provide a foundation for future large-scale benchmarking and further development of 3D displacement estimation techniques for LR-TLS data.

Structural health monitoring of water dams is crucial to ensure their long-term safety and operational reliability. Traditional geodetic techniques, although precise, are limited to sparse observation points and cannot capture spatially heterogeneous deformations. Laser scanning enables comprehensive, area-wide acquisition, overcoming this limitation. Subsequent deformation analysis often relies on comparisons along the local surface normal, which are limited in detecting in-plane movements. To address this, this study presents an approach that combines image-based stone segmentation with point-cloud-based deformation analysis to estimate both in-plane and out-of-plane displacements across masonry dam surfaces. Individual stones are detected in unmanned aerial vehicle (UAV) imagery using a deep learning segmentation model (Mask R-CNN) and subsequently projected into corresponding point clouds acquired by terrestrial laser scanning (TLS) and UAV laser scanning. By establishing consistent stone correspondences across multi-epoch point clouds via centroid-based matching and local iterative closest point (ICP) alignment, the proposed method enables deformation analysis on a stone-by-stone level. Simulated deformations were applied to TLS- and UAV-based point clouds of a dam to evaluate the method. Results demonstrate that the approach achieves sub-centimeter accuracy for the TLS and low-centimeter accuracy for the UAV point cloud, as measured by the RMSE between the estimated and true deformation. Our approach outperforms conventional model-to-model comparison methods, such as Multiscale Model to Model Cloud Comparison (M3C2), for in-plane displacements. The integration of image segmentation and geometric analysis provides a powerful framework for full-field deformation monitoring of masonry structures, supporting the detection of instabilities and improving dam safety.

In geodetic monitoring, terrestrial laser scanning (TLS) point clouds are typically assumed to be accurate and true-to-scale, implying that data acquired from different epochs or stations differ only by rigid transformations. Consequently, systematic errors related to scanner or platform variations can be mitigated through rigid point cloud registration. However, variations in the propagation speed and path of laser beams due to atmospheric refraction, as well as ranging biases induced by surface properties, can introduce non-rigid distortions in the generated point clouds. These effects are particularly pronounced under complex meteorological and topographic conditions, such as in mountainous areas. As a result, the acquired point clouds exhibit inhomogeneous and non-linear deviations that cannot be effectively compensated by simple distance corrections or rigid transformations. In this study, robust rigid registration is first performed to minimize the effects of platform offsets. A data-driven approach is then employed to generate sparse stable points, providing distance deviations that incorporate spatially varying systematic errors. Finally, a free-form surface is fitted to these sparse point-wise distance deviations, thereby establishing a 3D correction field for the entire point cloud. For a dataset collected by a permanent TLS monitoring system in the Vals Valley (Tyrol, Austria), the proposed method effectively reduces the registration residuals in TLS point clouds caused by inhomogeneous systematic errors.

Using point clouds captured by Terrestrial Laser Scanners for measurement tasks with high-quality requirements is well established in engineering geodesy. However, geometric imperfections within the scanners introduce systematic deviations into the captured point clouds. These deviations often reach several millimeters in magnitude, exceeding the impact of random measurement noise. Calibrating the scanners by estimating these internal imperfections allows these systematic errors to be corrected, thereby preventing misinterpretations of the measurement results. In this work, we develop a methodology that allows users of Terrestrial Laser Scanners to independently determine calibration parameters for panorama scanners and to correct the resulting point clouds using planar patches extracted directly from the captured data. This approach requires no additional hardware or specialized measurement equipment. We evaluate the methodology using an independent point cloud of a water dam and demonstrate that it achieves a substantial reduction in systematic deviations. Furthermore, by estimating calibration parameters in a dedicated state-of-the-art calibration field, we show that our method delivers results comparable to these established calibration procedures—yet without the need for such specialized calibration environments.

Information on the precision of TLS observables is limited. While the range measurement precision can be modeled with respect to the intensity measurement nowadays, the precision of the angular observations still relies on the claims of the manufacturer. This contribution proposes a method to determine the vertical angular variance of a TLS using profile measurements. Supported by a simulation, which serves as proof-of concept, the methodology is laid out. In the end, measurements with a Z+F IMAGER® 5016A are evaluated. A dependency of the angular standard deviation on the rotational speed of the beam deflection unit is observed. The estimation precision of the angular standard deviation is high with consistent values for differing ranges. The estimated angular standard deviations are much lower than the claims of the manufacturer starting with roughly 2" for the slowest rotating settings, up to 4" for the fastest. All this can be achieved by scanning a reflectivity target with at least two adjacent fields of different homogeneous reflectivity. This needs to be aligned to the scanner to reduce and eliminate as many contributing error sources as possible. The target itself provides the fields and the transitions needed to perform the in-situ estimation of the angular precision.