Integrative IRT for documentation and interpretation of archaeological structures

The documentation of built heritage involves tangible and intangible features. Several morphological and metric aspects of architectural structures are acquired throughout a massive data capture system, such as the Terrestrial Laser Scanner (TLS) and the Structure from Motion (SfM) technique. They produce models that give information about the skin of architectural organism. Infrared Thermography (IRT) is one of the techniques used to investigate what is beyond the external layer. This technology is particularly significant in the diagnostics and conservation of the built heritage. In archaeology, the integration of data acquired through different sensors improves the analysis and the interpretation of findings that are incomplete or transformed. Starting from a topographic and photogrammetric survey, the procedure here proposed aims to combine the bidimensional IRT data together with the 3D point cloud. This system helps to overcome the Field of View (FoV) of each IRT image and provides a three-dimensional reading of the thermal behaviour of the object. This approach is based on the geometric constraints of the pair of RGB-IR images coming from two different sensors mounted inside a bi-camera commercial device. Knowing the approximate distance between the two sensors, and making the necessary simplifications allowed by the low resolution of the thermal sensor, we projected the colour of the IR images to the RGB point cloud. The procedure was applied is the so-called Nymphaeum of Egeria, an archaeological structure in the Caffarella Park (Rome, Italy), which is currently part of the Appia Antica Regional Park

Bridge Structural Condition Assessment using 3D Imaging

Objective, accurate, and fast assessment of bridge structural condition is critical to timely assess safety risks. Current practices for bridge condition assessment rely on visual observations and manual interpretation of reports and sketches prepared by inspectors in the field. Visual observation, manual reporting and interpretation has several drawbacks such as being labor intensive, subject to personal judgment and experience, and prone to error. Terrestrial laser scanners (TLS) are promising sensors to automatically identify structural condition indicators, such as cracks, displacements and deflected shapes, as they are able to provide high coverage and accuracy at long ranges. However, there is limited research conducted on employing TLS to detect cracks for bridge condition assessment, which mainly focused on manual detection and measurements of cracks, displacements or shape deflections from the laser scan point clouds. TLS is an advance 3D imaging technology that is used to rapidly measure the 3D coordinates of densely scanned points within a scene. The data gathered by a TLS is provided in the form of 3D point clouds with color and intensity data often associated with each point within the cloud. This paper proposes a novel adaptive wavelet neural network (WNN) based approach to automatically detect concrete cracks from TLS point clouds for bridge structural condition assessment. The adaptive WNN is designed to selforganize, self-adapt, and sequentially learn a compact reconstruction of the 3D point cloud. The architecture of the network is based on a single-layer neural network consisting of Mexican hat wavelet functions. The approach was tested on a cracked concrete specimen. The preliminary experimental results show that the proposed approach is promising as it enables detecting concrete cracks accurately from TLS point clouds. Using the proposed method for crack detection would enable automatic and remote assessment of bridge condition. This would, in turn, result in reducing costs associated with infrastructure management, and improving the overall quality of our infrastructure by enhancing maintenance operations

AUTOMATIC FAÇADE SEGMENTATION FOR THERMAL RETROFIT

Abstract. In this paper we present an automated method to derive highly detailed 3D vector models of modern building facades from terrestrial laser scanning data. The developed procedure can be divided into two main steps: firstly the main elements constituting the facade are identified by means of a segmentation process, then the 3D vector model is generated including some priors on architectural scenes. The identification of main facade elements is based on random sampling and detection of planar elements including topology information in the process to reduce under- and over-segmentation problems. Finally, the prevalence of straight lines and orthogonal intersections in the vector model generation phase is exploited to set additional constraints to enforce automated modeling. Contemporary a further classification is performed, enriching the data with semantics by means of a classification tree. The main application field for these vector models is the design of external insulation thermal retrofit. In particular, in this paper we present a possible application for energy efficiency evaluation of buildings by mean of Infrared Thermography data overlaid to the facade model

Mosaicking thermal images of buildings

Nowadays several thermal cameras capture images based on a pinhole camera model. This paper shows how multiple images of flat-like objects or 3D bodies can be mapped and mosaicked with a mathematical formulation between image and object spaces. This work demonstrates that both geometric and radiometric parts need proper mathematical models that allow the user to obtain a global product (orthophotos or 3D models) where accurate and detailed photogrammetric models and thermal images are registered in order to combine geometry and thermal information

Finding buried remains using thermal images

This paper presents the use of an Unmanned Aerial Vehicle (UAV) platform for the inspection and documentation of historical buried structures, starting from the landscape scale up to the local and detailed scale. The combined use of RGB and thermal images acquired from this kind of platforms, along with algorithms and procedures for data registration, can be a quick and powerful contact-less methodology to discover hidden structures. In this case study, the identification of buried remains needed the implementation of new algorithms able to register thermal images with the geometrical survey from RGB data. The georeferenced images (thermal orthophotos) were then used to inspect the ground and discover buried features. </jats:p

Integration of infrared thermography and photogrammetric surveying of built landscape

The thermal analysis of buildings represents a key-step for reduction of energy consumption, also in the case of Cultural Heritage. Here the complexity of the constructions and the adopted materials might require special analysis and tailored solutions. Infrared Thermography (IRT) is an important non-destructive investigation technique that may aid in the thermal analysis of buildings. The paper reports the application of IRT on a listed building, belonging to the Cultural Heritage and to a residential one, as a demonstration that IRT is a suitable and convenient tool for analysing the existing buildings. The purposes of the analysis are the assessment of the damages and energy efficiency of the building envelope. Since in many cases the complex geometry of historic constructions may involve the thermal analysis, the integration of IRT and accurate 3D models were developed during the latest years. Here authors propose a solution based on the up-to-date photogrammetric solutions for purely image-based 3D modelling, including automatic image orientation/sensor calibration using Structure-from-Motion and dense matching. Thus, an almost fully automatic pipeline for the generation of accurate 3D models showing the temperatures on a building skin in a realistic manner is described, where the only manual task is given by the measurement of a few common points for co-registration of RGB and IR photogrammetric projects.Xunta de Galicia | Ref. ED481B 2016/079-

Accuracy analysis of a mobile mapping system for close range photogrammetric projects

[EN] Image-based mapping solutions require accurate exterior orientation parameters independently of the cameras used for a survey. This paper analyses the inclusion of up to two stereo-based geometric constraints in the form of baseline distance and convergence angle between camera axes to boost the integrated sensor orientation performance on outdoor close-range projects. A terrestrial low-cost mobile mapping GNSS/IMU multi-camera system is used to test the performance of the stereo-based geometric constraint on a weak geometric network in a stop-and-go survey. The influence of the number of control points (CPs) is analysed to confirm the performance and usability of the geometric constraints in real live terrestrial projects where far from ideal setups can exist across the survey. Improvements in image residuals up to 9 times and deviation errors better than 1 cm are expected when at least three CPs are incorporated into the adjustmentThe authors gratefully acknowledge the support from the Spanish Ministerio de Economia y Competitividad to the project HAR2014-59873-R. Contributions on direct georeferencing from professors Dr. David Hernandez-Lopez, Dr. Luis Garcia-Asenjo and D. Pascual Garrigues are highly appreciated.Navarro Tarin, S.; Lerma García, JL. (2016). Accuracy analysis of a mobile mapping system for close range photogrammetric projects. Measurement. 93:148-156. https://doi.org/10.1016/j.measurement.2016.07.0301481569

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

3D INTERPRETATION AND FUSION OF MULTIDISCIPLINARY DATA FOR HERITAGE SCIENCE: A REVIEW

Activities related to the protection of tangible heritage require extensive multidisciplinary documentation. The various raw data that occur have been oftentimes been processed, visualized and evaluated separately leading to aggregations of unassociated information of varying data types. In the direction of adopting complete approaches towards more effective decision making, the interpretation and fusion of these data in three dimensions, inserting topological information is deemed necessary. The present study addresses the achieved level of three-dimensional interpretation and fusion with geometric models of data originating from different fields, by providing an extensive review of the relevant literature. Additionally, it briefly discusses perspectives on techniques that could potentially be integrated with point clouds or models

Terrestrial Laser Scanning-Based Bridge Structural Condition Assessment

Objective, accurate, and fast assessment of a bridge’s structural condition is critical to the timely assessment of safety risks. Current practices for bridge condition assessment rely on visual observations and manual interpretation of reports and sketches prepared by inspectors in the field. Visual observation, manual reporting, and interpretation have several drawbacks, such as being labor intensive, subject to personal judgment and experience, and prone to error. Terrestrial laser scanners (TLS) are promising sensors for automatically identifying structural condition indicators, such as cracks, displacements, and deflected shapes, because they are able to provide high coverage and accuracy at long ranges. However, limited research has been conducted on employing laser scanners to detect cracks for bridge condition assessment, and the research has mainly focused on manual detection and measurement of cracks, displacements, or shape deflections from the laser scan point clouds. This research project proposed to measure the performance of TLS for the automatic detection of cracks for bridge structural condition assessment. Laser scanning is an advanced imaging technology that is used to rapidly measure the three-dimensional (3D) coordinates of densely scanned points within a scene. The data gathered by a laser scanner are provided in the form of point clouds, with color and intensity data often associated with each point within the cloud. Point cloud data can be analyzed using computer vision algorithms to detect cracks for the condition assessment of reinforced concrete structures. In this research project, adaptive wavelet neural network (WNN) algorithms for detecting cracks from laser scan point clouds were developed based on the state-of-the-art condition assessment codes and standards. Using the proposed method for crack detection would enable automatic and remote assessment of a bridge’s condition. This would, in turn, result in reducing the costs associated with infrastructure management and improving the overall quality of our infrastructure by enhancing maintenance operations