Search Results (183)

Given its considerable cultural, historical, and economic value, built heritage requires the application of modern techniques for effective documentation and conservation. While multiple sensors are available for 3D modeling, laser scanning remains the most commonly employed due to its efficiency, precision, and ability to comprehensively capture the building’s geometry, surface textures, and structural details. This results in highly detailed 3D representations that are very important for accurate documentation, analysis, and conservation planning. This study investigates the complementary potential of different 3D modeling approaches for the digital representation of the Dosoftei House in Iasi, a monument of historical significance. For this purpose, an integrated point cloud was created based on a mobile hand-held laser scanner (HMLS), i.e., the FJD Trion P1 and a terrestrial laser scanner (TLS), i.e., the Maptek I-Site 8820 long-range laser scanner, the latter specifically used to capture the roof structures. Based on this dataset, a parametric model was created in Revit, supported by panoramic images, allowing for a structured representation useful in technical documentation and heritage management. In parallel, a mesh model was generated in CloudCompare using Poisson surface reconstruction. The comparison of the two methods highlights the high geometric accuracy of the mesh model and the Building Information Modeling (BIM) model’s capability to efficiently manage information linked to architectural elements. While the mesh provides detailed geometry, the BIM model excels in information organization and supports informed decision-making in conservation efforts. This research proposes leveraging the advantages of both methods within an integrated workflow, applicable on a larger scale in architectural heritage conservation projects. Full article

The digitization and semantic enrichment of built environments traditionally rely on costly and labor-intensive processes, which hinder scalability, adaptability, and real-time deployment in real-world applications. This research presents a novel, fully automated approach that transforms single RGB images directly into semantically enriched, Building Information Modeling (BIM)-compatible 3D representations via an innovative domain adaptation and multi-task learning pipeline. The proposed method simultaneously leverages depth estimation and semantic segmentation from single-image inputs, using high-capacity 2D neural networks, thereby enabling accurate 3D mesh reconstruction and semantic labeling without manual annotation or specialized sensors. The developed pipeline segments and reconstructs both common architectural elements and previously unrepresented object classes, such as stairs, balustrades, railings, people, and furniture items, expanding the coverage of existing 3D indoor datasets. Experimental evaluations demonstrate remarkable reconstruction precision, with an RMSE as low as 0.02 and a per-point semantic accuracy of 81.89% on the TUM CMS Indoor Point Clouds dataset. The resulting 3D models are directly exportable to BIM, OBJ, and CAD formats, supporting a wide range of applications including digital documentation, asset management, and digital twins. By achieving high accuracy and semantic richness with minimal input, the proposed framework offers a scalable, efficient, and automated solution for the rapid digitization of complex built environments, addressing critical limitations in traditional scan-to-BIM workflows and setting new performance standards for future research in the field. Full article

Accurate and efficient rock mass characterization is essential in geotechnical engineering, yet traditional tunnel face mapping remains time consuming, subjective, and potentially hazardous. Recent advances in digital technologies and AI offer automation opportunities, but many existing solutions are hindered by slow 3D scanning, computationally intensive processing, and limited integration flexibility. This paper presents Tunnel Rapid AI Classification (TRaiC), an open-source MATLAB-based platform for rapid and automated tunnel face mapping. TRaiC integrates single-shot 360° panoramic photography, AI-powered discontinuity detection, 3D textured digital twin generation, rock mass discontinuity characterization, and Retrieval-Augmented Generation with Large Language Models (RAG-LLM) for automated geological interpretation and standardized reporting. The modular eight-stage workflow includes simplified 3D modeling, trace segmentation, 3D joint network analysis, and rock mass classification using RMR, with outputs optimized for Geo-BIM integration. Initial evaluations indicate substantial reductions in processing time and expert assessment workload. Producing a lightweight yet high-fidelity digital twin, TRaiC enables computational efficiency, transparency, and reproducibility, serving as a foundation for future AI-assisted geotechnical engineering research. Its graphical user interface and well-structured open-source code make it accessible to users ranging from beginners to advanced researchers. Full article

This research advances the integration of terrestrial laser scanning (TLS) in heritage documentation, targeting the development of holistic and practical guidance for practitioners to adopt the technology effectively. Acknowledging the pivotal role of TLS in capturing detailed and accurate representations of cultural heritage, the study emerges against a backdrop of technological progression and the evolving needs of heritage conservation. Through a comprehensive literature review, critical case studies of heritage sites in the U.S., expert interviews, and the development of a TLS for Heritage Documentation Best Practice Guide (the guide), the paper addresses the existing gaps in streamlined practices in the domain of TLS’s applications in heritage documentation. While recognizing and building upon foundational efforts such as international guidelines developed over the past decades, this study contributes a practice-oriented perspective grounded in field experience and case-based analysis. The developed guide seeks to equip practitioners with structured methods and practical tools to optimize the use of TLS, ultimately enhancing the quality and accessibility of heritage documentation. It also sets a foundation for integrating TLS datasets with other technologies, such as Building Information Modeling (BIM), virtual reality (VR), and augmented reality (AR) for heritage preservation, tourism, education, and interpretation, ultimately enhancing access to and engagement with cultural heritage sites. The paper also critically situates this guidance within the evolving theoretical discourse on digital heritage practices, highlighting its alignment with and divergence from existing methodologies. Full article

Point clouds acquired by mobile laser scanners (MLS) are widely used for generating as-built building information models (BIM), particularly in indoor construction environments and existing buildings. While MLS offers fast and efficient scanning through SLAM technology, its accuracy and precision remains lower than that of terrestrial laser scanners (TLS). This study investigates the potential to improve MLS-based as-built BIM accuracy by analyzing and utilizing error distribution patterns inherent in MLS point clouds. Based on the assumption that each MLS device exhibits consistent and unique error distribution patterns, an experiment was conducted using three MLS devices and TLS-derived reference data. The analysis employed iterative closest point (ICP) registration and cloud-to-mesh (C2M) distance measurements on mock-ups with closed shapes. The results revealed that error patterns were stable across scans and could be leveraged as correction factors. In other words, the results indicate that when using MLS for as-built BIM generation, robust fitting methods have limitations in obtaining realistic object dimensions, as they do not account for the unique error patterns present in MLS point clouds. The proposed method provides a simple and repeatable approach for enhancing MLS accuracy, contributing to improved dimensional reliability in MLS-driven BIM applications. Full article

Technological development has strongly impacted all processes related to the design, construction, and management of real estate assets. In fact, the introduction of the BIM approach has required the application of three-dimensional survey technologies, and in particular the use of LiDAR instruments, both in their static (TLS—terrestrial laser scanner) and dynamic (iMMS—indoor mobile mapping system) implementations. Operators and developers of LiDAR technologies, for the implementation of scan-to-BIM procedures, initially placed particular care on the 3D surveying accuracy obtainable from such tools. The incorporation of RGB sensors into these instruments has progressively expanded LiDAR-based applications from essential topographic surveying to geospatial applications, where the emphasis is no longer on the accurate three-dimensional reconstruction of buildings but on the capability to create three-dimensional image-based visualizations, such as virtual tours, which allow the recognition of assets located in every area of the buildings. Although much has been written about obtaining the best possible accuracy for extensive asset surveying of large-scale building complexes using iMMS systems, it is now essential to develop and define suitable procedures for controlling such kinds of surveying, targeted at specific geospatial applications. We especially address the design, field acquisition, quality control, and mass data management techniques that might be used in such complex environments. This work aims to contribute by defining the technical specifications for the implementation of geospatial mapping of vast asset survey activities involving significant building sites utilizing iMMS instrumentation. Three-dimensional models can also facilitate virtual tours, enable local measurements inside rooms, and particularly support the subsequent integration of self-locating image-based technologies that can efficiently perform field updates of surveyed databases. Full article

The conservation and intervention of heritage structures require a flexible, interdisciplinary environment capable of managing data throughout the building’s life cycle. Historic building information modeling (HBIM) has emerged as an effective tool for supporting these processes. Originally conceived for parametric construction modeling, BIM can also integrate historical transformations, aiding in maintenance and preservation. Historic buildings often feature complex geometries and visible material traces of time, requiring detailed analysis. This research proposes a methodology for documenting and assessing the envelope of historic buildings by locating, classifying, and recording transformations, deterioration, and structural deformations. The approach is based on semantic segmentation and classification using data from terrestrial laser scanning (TLS) and unmanned aerial vehicles (UAVs), applied to the Palace of Miguel de Mañara—an iconic 17th-century building in Seville. Archival images were integrated into the HBIM model to identify previous restoration interventions and assess current deterioration. The methodology included geometric characterization, material mapping, semantic segmentation, diagnostic input, and temporal analysis. The results validated a process for detecting pathological cracks in masonry facades, providing a collaborative HBIM framework enriched with expert-validated data to support repair decisions and guide conservation efforts. Full article

The integration of computer vision and deep learning into Building Information Modeling (BIM) workflows has created a growing need for structured datasets that enable the semantic segmentation of indoor building elements. This paper presents StructScan3D v1, the first version of an RGB-D dataset specifically designed to facilitate the automated segmentation and modeling of architectural and structural components. Captured using the Kinect Azure sensor, StructScan3D v1 comprises 2594 annotated frames from diverse indoor environments, including residential and office spaces. The dataset focuses on six key building elements: walls, floors, ceilings, windows, doors, and miscellaneous objects. To establish a benchmark for indoor RGB-D semantic segmentation, we evaluate D-Former, a transformer-based model that leverages self-attention mechanisms for enhanced spatial understanding. Additionally, we compare its performance against state-of-the-art models such as Gemini and TokenFusion, providing a comprehensive analysis of segmentation accuracy. Experimental results show that D-Former achieves a mean Intersection over Union (mIoU) of 67.5%, demonstrating strong segmentation capabilities despite challenges like occlusions and depth variations. As an evolving dataset, StructScan3D v1 lays the foundation for future expansions, including increased scene diversity and refined annotations. By bridging the gap between deep learning-driven segmentation and real-world BIM applications, this dataset provides researchers and practitioners with a valuable resource for advancing indoor scene reconstruction, robotics, and augmented reality. Full article

This study integrates quantitative scientometric analysis with a qualitative systematic review to comprehensively examine the evolution, core research themes, and emerging trends of three-dimensional (3D) visualization technology in architectural heritage conservation from 2005 to 2024. A total of 813 relevant publications were retrieved from the Web of Science Core Collection and analyzed using CiteSpace to construct a detailed knowledge map of the field. The findings highlight that foundational technologies such as terrestrial laser scanning (TLS), photogrammetry, building information modeling (BIM), and heritage building information modeling (HBIM) have laid a solid technical foundation for accurate heritage documentation and semantic representation. At the same time, the integration of digital twins, the Internet of Things (IoT), artificial intelligence (AI), and immersive technologies has facilitated a shift from static documentation to dynamic perception, real-time analysis, and interactive engagement. The analysis identifies four major research domains: (1) 3D data acquisition and modeling techniques, (2) digital heritage documentation and information management, (3) virtual reconstruction and interactive visualization, and (4) digital transformation and cultural narrative integration. Based on these insights, this study proposes four key directions for future research: advancing intelligence and automation in 3D modeling workflows; enhancing cross-platform interoperability and semantic standardization; realizing the full lifecycle management of architectural heritage; and enhancing cultural narratives through digital expression. This study provides a systematic and in-depth understanding of the role of 3D visualization in architectural heritage conservation. It offers a solid theoretical foundation and strategic guidance for technological innovation, policy development, and interdisciplinary collaboration in the digital heritage field. Full article

Historic Cairo, recognized as a UNESCO World Heritage Site in 1979, is renowned for its rich Islamic architecture, including sabils, which have played a crucial role in the urban fabric of this arid region. This study focuses on the oldest surviving Ottoman sabil in Cairo—the Sabil and Kutab of Khusru Pasha—as a case study for digital heritage preservation using advanced documentation technologies. We propose a flexible, dynamic documentation workflow based on the heritage digital twin (HDT) framework, which integrates both physical and digital-native processes. Through a hybrid methodology that combines 3D laser scanning, photogrammetry, and building information modeling (BIM), this study aims to transition from static heritage record-keeping to an interactive, semantically structured digital representation. This approach enhances the efficiency and accuracy of documentation, supports long-term conservation, and facilitates immersive public engagement. Quantitative data, including scan resolution and processing time, are used to assess the effectiveness of the adopted workflow. The digital twin created from this case study offers a replicable model for safeguarding similar mid-scale heritage assets across Islamic Cairo. Furthermore, integrating HDTs into virtual tourism frameworks creates new possibilities for cultural accessibility, education, and sustainable tourism development. By illustrating how historical buildings like the Khusru Pasha Sabil can be virtually preserved, monitored, and promoted, this study highlights the transformative potential of digital twin technology in heritage conservation. It contributes to the evolving discourse on smart documentation and management strategies, aligning with global sustainability goals and digital heritage preservation initiatives. Full article

Since the early 21st century, BIM technology has enhanced building design, construction and management, while continuously evolving to create new specializations. Despite this, its full potential remains untapped. Today, BIM offers diverse applications in construction and related industries, incorporating advanced techniques such as laser scanning and photogrammetry. A specialized approach, HBIM (Heritage Building Information Modeling), enables the digital mapping, documentation, analysis and management of historic architecture. This study focuses on the Koprowski Family Villa in Celestynów, known as “Koprówka”, demolished twenty years ago. Despite its cultural significance, the property disappeared from the village. Using LiDAR survey data, preserved window frames, archival photographs and documents, this engineering study reconstructs “Koprówka” as an HBIM model, integrated into the existing landscape. The resulting 3D model can aid municipal authorities and potential investors in rebuilding “Koprówka”, while emphasizing the importance of cultural heritage in shaping local identity and raising awareness of historical structures’ value. Full article

Sports facilities encompass diverse spaces tailored to various sports disciplines, each characterized by unique shapes and sizes. Skateparks, renowned for their avant-garde designs, are meticulously crafted to exude distinctiveness, featuring an array of constructions, surfaces, and intricate shapes. Traditional measurement methods often struggle to capture the spatial, structural, and architectural diversity of these facilities. Constructing 3D models, particularly with Building Information Modeling (BIM) technology, faces inherent challenges due to the complex and individualistic nature of skateparks. The crux lies in acquiring credible and comprehensive spatial and construction-related information. Geospatial data emerges as a viable solution, effectively addressing the skatepark’s myriad forms while upholding information accuracy and reliability. By gathering, processing, and integrating Terrestrial Laser Scanning and drone-based photogrammetry point cloud data, a precise spatial foundation is established for BIM model generation. Leveraging the integrated point cloud and photographic data aids in identifying elements and construction materials, facilitating the creation of detailed technical documentation and life-like visualizations. This not only supports condition assessment and maintenance planning, but also assists in strategically planning facility expansions, renovations, or component replacements. Moreover, BIM technology streamlines facility information management by preserving vital object-related data in a structured database, enhancing overall efficiency and effectiveness. Full article

The aim of the paper is to identify energy inefficiencies and to propose energy-saving solutions to reduce the carbon footprint of the structures, considering Building Information Modelling—Existing Conditions Model as an enabling tool. The initiative underscores the role of Building Information Modelling—Existing Conditions Model in facilitating data-driven strategies for improving energy efficiency, highlighting its potential to transform the infrastructure and built environment into a paradigm of responsible energy consumption. In this context, the paper presents a comprehensive assessment of the thermal performance of student accommodations using advanced technologies, such as Unmanned Aerial Systems and Terrestrial Laser Scanners equipped with thermal cameras. The findings illustrate potential areas for improvements in thermal efficiency, offering a roadmap for targeted interventions to enhance the energy performance of buildings. The results of the study not only advance the green campus at Politehnica University Timisoara (Romania), but also serve as an educational model that demonstrates the integration of technology to promote sustainability in the built environment. Full article

In a construction industry characterized by rapid change and increasing complexity, traditional labor-intensive methods of structural safety inspection suffer from numerous limitations, including low productivity, extended project duration, and high costs. Accuracy and speed are particularly vital for safety diagnosis in older structures, fire-damaged buildings, and large-scale facilities, yet conventional methods heavily rely on human expertise, leading to variability and potential unreliability of results. This study quantitatively assessed the productivity improvements and economic benefits of integrating terrestrial laser scanning (TLS) and building information modeling (BIM) into the structural safety assessment process. Both the conventional visual inspection and the TLS-based approach were treated as production systems, and queueing theory was employed to analyze waiting times, service rates, and costs. The analysis of three real-world projects revealed that the TLS-based 3D scanning reverse modeling method reduces total project costs—including survey duration, labor input, as well as service and waiting costs—by about 80–89% compared to the visual inspection method. Additionally, the return on investment (ROI) at the project level varies from approximately 12% to 136%, and when applied to multiple projects at the company level, the overall ROI reaches about 162%, demonstrating the long-term economic benefits of adopting digital technology. These findings suggest that TLS-3D scanning reverse modeling in the construction industry has substantial potential in terms of productivity gains and economic impact. Full article

This study presents a comprehensive framework for evaluating urban heat resilience, incorporating urban climatology models, their characteristics, and simulation programs. Utilizing the local climate zone (LCZ) classification method, this research explores how urban geomorphology influences the thermal characteristics of the area. This study integrates spatial data at different “levels of detail” (LOD), from the meso- to building scales, emphasizing the significance of detailed LOD 3 models acquired through 3D laser scanning. The results demonstrate the ability of these models to identify urban heat islands (UHIs) and to simulate urban planning scenarios, such as increasing green spaces and optimizing building density, to mitigate the UHI effect. The ST3D 3D model of the city of Split, represented using an LOD 2 object model, is utilized for meso- and local-scale analyses, while LOD 3 models derived from laser scanning provided in-depth insights at the building scale. The case studies included the Faculty of Civil Engineering, Architecture, and Geodesy building on the University of Split campus and the old town hall in the densely built city center. This framework highlights the advantages of integrating GIS and BIM technology with urban climate analyses, offering tools for data-driven decision-making and fostering sustainable, climate-resilient urban planning. Full article