Lidar data analyses for assessing the conservation status of the so-called baths-church in hierapolis of phrygia (TR)

The LiDAR technology has aroused considerable interest in the field of structural study of historical buildings, aimed at the structural assessment in the presence of different states of stresses and at the evaluation of the health status. The interest is due mostly by the ability of generating models of the built structures being able to predetermine different levels of schematization, two-dimensional and three-dimensional, in order to be able to perform evaluation processes assigning simplified geometric contents that correspond to the physical reality of the artefacts. This paper intends to report some results of these experiences applied in archaeological domain, to the so-called Baths-Church at Hierapolis in Phrygia (Pamukkale, TR). In particular, the generation of accurate models from dense clouds and their reduction to models with simplified geometries too, is explored, with the further aim of testing automated strategies for features detection and editing process that leads to appropriate models for visual and analytical structural assessment. The accuracy and density parameters of the LiDAR clouds will be analysed to derive orthophotos and continuous mesh models, both to obtain the best results from the application of research algorithms such as region growing to detect blocks, and to allow visual analysis on digital models and not on site. The ability to determine with high accuracy both the size and the anomalies of the wall systems (out of plumb and other rotation or local mechanisms of collapse), together with the possibility of identifying the lay of the individual drywall blocks and also the signs of cracks and collapses, allow deriving suitable models both for FE (Finite Elements) analysis and DE (Discrete Elements) analysis, as well as analytical ones

Automatic morphologic analysis of quasi-periodic masonry walls from LiDAR

This article presents a novel segmentation algorithm that allows the automatic segmentation of masonry blocks from a 3D point cloud acquired with LiDAR technology, for both stationary and mobile devices. The point cloud segmentation algorithm is based on a 2.5D approach that creates images based on the intensity attribute of LiDAR systems. Image processing algorithms based on an improvement of the marked-controlled watershed was successfully used to produce the automatic segmentation of the point cloud in the 3D space isolating each individual stone block. Finally, morphologic analysis in two case studies has been carried out. The morphologic analysis provides information about the assemblage of masonry pieces which is valuable for the structural evaluation of masonry buildings.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (Ref.: TIN201346801-C4-4-R) and by Xunta de Galicia (Grant No. CN2012/269 and Grant No. EM2013/005). Authors want to give thanks to the reviewers for their constructive comments that contributed to improve both the method and the presentation of results

Machine Learning for the Built Heritage Archaeological Study

The presence of artificial intelligence in our lives is increasing and being applied to fields such as medicine, engineering, telecommunications, remote sensing and 3D visualization. Nevertheless, it has never been used for the stratigraphic study of historical buildings. Thus far, archaeologists and architects, the experts in archaeology of architecture, have led this research. The method consisted of visually-and, consequently, subjectively-identifying certain evidence regarding the elevations of such buildings that could be a consequence of the passage of time. In this article, we would like to present the results from one of the research projects pursued by our group, in which we automated the stratigraphic study of some historic buildings using multivariate statistic techniques. To this end, we first measured the building using surveying techniques to create a 3D model, and then, we broke down every stone into qualitative and quantitative variables. To identify the stratigraphic features on the walls, we applied machine learning by conducting different predictive and descriptive analyses. The predictive analyses were used to rule out any blocks of stone with different characteristics, such as rough stones, joint ashlars, and voussoirs of arches; these are irregularities that probably show building processes and whose identification is crucial in ascertaining the structural evolution of the building. In supervised learning, we experimented with decision trees and random forest- and although the results were good in all cases, we ultimately opted to implement the predictive model obtained using the last one. While identifying the evidence on the walls, it was also very important to identify different continuity solutions or interfaces present on them, because although these are elements without materiality, they are of great value in terms of timescale, because they delimit different strata and allow us to deduce the relationship between them.Archaeology of Architecture in the old and the new world: from the stratigraphy of the buildings to the stratigraphy of the urban fabric"(PID2019-109464GB-I00), financed by the Spanish Ministry of Science and Innovation

CNN-Based Watershed Marker Extraction for Brick Segmentation in Masonry Walls

Nowadays there is an increasing need for using artificial intelligence techniques in image-based documentation and survey in archeology, architecture or civil engineering applications. Brick segmentation is an important initial step in the documentation and analysis of masonry wall images. However, due to the heterogeneous material, size, shape and arrangement of the bricks, it is highly challenging to develop a widely adoptable solution for the problem via conventional geometric and radiometry based approaches. In this paper, we propose a new technique which combines the strength of deep learning for brick seed localization, and the Watershed algorithm for accurate instance segmentation. More specifically, we adopt a U-Net-based delineation algorithm for robust marker generation in the Watershed process, which provides as output the accurate contours of the individual bricks, and also separates them from the mortar regions. For training the network and evaluating our results, we created a new test dataset which consist of 162 hand-labeled images of various wall categories. Quantitative evaluation is provided both at instance and at pixel level, and the results are compared to two reference methods proposed for wall delineation, and to a morphology based brick segmentation approach. The experimental results showed the advantages of the proposed U-Net markered Watershed method, providing average F1-scores above 80%

FROM 2D TO 3D SUPERVISED SEGMENTATION AND CLASSIFICATION FOR CULTURAL HERITAGE APPLICATIONS

The digital management of architectural heritage information is still a complex problem, as a heritage object requires an integrated representation of various types of information in order to develop appropriate restoration or conservation strategies. Currently, there is extensive research focused on automatic procedures of segmentation and classification of 3D point clouds or meshes, which can accelerate the study of a monument and integrate it with heterogeneous information and attributes, useful to characterize and describe the surveyed object. The aim of this study is to propose an optimal, repeatable and reliable procedure to manage various types of 3D surveying data and associate them with heterogeneous information and attributes to characterize and describe the surveyed object. In particular, this paper presents an approach for classifying 3D heritage models, starting from the segmentation of their textures based on supervised machine learning methods. Experimental results run on three different case studies demonstrate that the proposed approach is effective and with many further potentials

Deep Learning-Based Masonry Wall Image Analysis

In this paper we introduce a novel machine learning-based fully automatic approach for the semantic analysis and documentation of masonry wall images, performing in parallel automatic detection and virtual completion of occluded or damaged wall regions, and brick segmentation leading to an accurate model of the wall structure. For this purpose, we propose a four-stage algorithm which comprises three interacting deep neural networks and a watershed transform-based brick outline extraction step. At the beginning, a U-Net-based sub-network performs initial wall segmentation into brick, mortar and occluded regions, which is followed by a two-stage adversarial inpainting model. The first adversarial network predicts the schematic mortar-brick pattern of the occluded areas based on the observed wall structure, providing in itself valuable structural information for archeological and architectural applications. The second adversarial network predicts the pixels’ color values yielding a realistic visual experience for the observer. Finally, using the neural network outputs as markers in a watershed-based segmentation process, we generate the accurate contours of the individual bricks, both in the originally visible and in the artificially inpainted wall regions. Note that while the first three stages implement a sequential pipeline, they interact through dependencies of their loss functions admitting the consideration of hidden feature dependencies between the different network components. For training and testing the network a new dataset has been created, and an extensive qualitative and quantitative evaluation versus the state-of-the-art is given. The experiments confirmed that the proposed method outperforms the reference techniques both in terms of wall structure estimation and regarding the visual quality of the inpainting step, moreover it can be robustly used for various different masonry wall types