3D Data Fusion for Historical Analyses of Heritage Buildings Using Thermal Images: The Palacio de Colomina as a Case Study

In the framework of built heritage monitoring techniques, a prominent position is occupied by thermography, which represents an efficient and non-invasive solution for these kinds of investigations, allowing the identification of phenomena detectable only in the non-visible range of the electromagnetic spectrum. This is of extreme interest, especially considering the possibility of integrating the radiometric information with the 3D models achievable from laser scanning or photogrammetric techniques, characterised by a high spatial resolution. This paper aims to illustrate how combining different geomatics techniques (in particular, by merging thermal images, laser scanning point clouds, and traditional visible colour photogrammetric data) can efficiently support historical analyses for studying heritage buildings. Additionally, a strategy for generating HBIM models starting from the integration of 3D thermal investigations and historical sources is proposed, concerning both the multi-temporal modification of the volumes of the building and the individual architectural elements. The case study analysed for the current research was the Palacio de Colomina in Valencia, Spain, a noble palace—now the headquarters of a university—that, during the last few centuries, has been subjected to considerable transformations in terms of rehabilitation works and modification of its volume

3D Thermal Mapping of Architectural Heritage

The combination of thermographic and geometric recording has always been an issue for architectural heritage diagnostic investigations. Multidisciplinary projects often require integrating multi-sensor information—including metric and temperature data—to extract valid conclusions regarding the state-of-preservation of historical buildings. Towards this direction, recent technological advancements in thermographic cameras and three-dimensional (3D) documentation instrumentation and software have contributed significantly, assisting the rapid creation of detailed 3D thermal-textured results, which can be exploited for non-destructive diagnostical surveys. This paper aims to briefly review and evaluate the current workflows for thermographic architectural 3D modeling, which implement state-of-the-art sensing procedures and processing techniques, while also presenting some applications on case studies of significant heritage value to help discuss current problems and identify topics for relevant future research

Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies

Urban environments are regions of complex and diverse architecture. Their reconstruction and representation as three-dimensional city models have attracted the attention of many researchers and industry specialists, as they increasingly recognise the potential for new applications requiring detailed building models. Nevertheless, despite being investigated for a few decades, the comprehensive reconstruction of buildings remains a challenging task. While there is a considerable body of literature on this topic, including several systematic reviews summarising ways of acquiring and reconstructing coarse building structures, there is a paucity of in-depth research on the detection and reconstruction of façade openings (i.e., windows and doors). In this review, we provide an overview of emerging applications, data acquisition and processing techniques for building façade reconstruction, emphasising building opening detection. The use of traditional technologies from terrestrial and aerial platforms, along with emerging approaches, such as mobile phones and volunteered geography information, is discussed. The current status of approaches for opening detection is then examined in detail, separated into methods for three-dimensional and two-dimensional data. Based on the review, it is clear that a key limitation associated with façade reconstruction is process automation and the need for user intervention. Another limitation is the incompleteness of the data due to occlusion, which can be reduced by data fusion. In addition, the lack of available diverse benchmark datasets and further investigation into deep-learning methods for façade openings extraction present crucial opportunities for future research

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Combined Use of Terrestrial Laser Scanning and IR Thermography Applied to a Historical Building

Abstract: The conservation of architectural heritage usually requires a multidisciplinary approach involving a variety of specialist expertise and techniques. Nevertheless, destructive techniques should be avoided, wherever possible, in order to preserve the integrity of the historical buildings, therefore the development of non-destructive and non-contact techniques is extremely important. In this framework, a methodology for combining the terrestrial laser scanning and the infrared thermal images is proposed, in order to obtain a reconnaissance of the conservation state of a historical building. The proposed case study is represented by St. Augustine Monumental Compound, located in the historical centre of the town of Cosenza (Calabria, South Italy). Adopting the proposed methodology, the paper illustrates the main results obtained for the building test overlaying and comparing the collected data with both techniques, in order to outline the capabilities both to detect the anomalies and to improve the knowledge on health state of the masonry building. The 3D model, also, allows to provide a reference model, laying the groundwork for implementation of a monitoring multisensor system based on the use of non-destructive techniques

Machine learning methods in BIM-based applications : a review

This paper presents a survey of machine learning (ML) methods used in applications dedicated to the building and construction industry. A building information modeling (BIM) model, being a database system for civil engineering data, is presented. A representative selection of methods and applications is described. The aim of this paper is to facilitate the continuation of research efforts and to encourage bigger participation of database system researchers in the field of civil engineering

Remote sensing image fusion on 3D scenarios: A review of applications for agriculture and forestry

Three-dimensional (3D) image mapping of real-world scenarios has a great potential to provide the user with a more accurate scene understanding. This will enable, among others, unsupervised automatic sampling of meaningful material classes from the target area for adaptive semi-supervised deep learning techniques. This path is already being taken by the recent and fast-developing research in computational fields, however, some issues related to computationally expensive processes in the integration of multi-source sensing data remain. Recent studies focused on Earth observation and characterization are enhanced by the proliferation of Unmanned Aerial Vehicles (UAV) and sensors able to capture massive datasets with a high spatial resolution. In this scope, many approaches have been presented for 3D modeling, remote sensing, image processing and mapping, and multi-source data fusion. This survey aims to present a summary of previous work according to the most relevant contributions for the reconstruction and analysis of 3D models of real scenarios using multispectral, thermal and hyperspectral imagery. Surveyed applications are focused on agriculture and forestry since these fields concentrate most applications and are widely studied. Many challenges are currently being overcome by recent methods based on the reconstruction of multi-sensorial 3D scenarios. In parallel, the processing of large image datasets has recently been accelerated by General-Purpose Graphics Processing Unit (GPGPU) approaches that are also summarized in this work. Finally, as a conclusion, some open issues and future research directions are presented.European Commission 1381202-GEU PYC20-RE-005-UJA IEG-2021Junta de Andalucia 1381202-GEU PYC20-RE-005-UJA IEG-2021Instituto de Estudios GiennesesEuropean CommissionSpanish Government UIDB/04033/2020DATI-Digital Agriculture TechnologiesPortuguese Foundation for Science and Technology 1381202-GEU FPU19/0010