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.