The current research aims at investigating the potential of Image Segmentation (IS) as a data source for mapping, with a bottom-up approach, the spatial quality of slow routes, localized in the territories “in-between” the main cities. The paper analyses two different case studies in Lombardy and Molise regions, where a different territorial configuration and data are available. The IS method, that computes area percentages in the street-level imagery by using Pixellab/TensorFlow digital environment, has been applied for detecting three different environments that are intersected by the selected routes and that are also detectable by using GIS tools: open spaces, built environment and rows of trees. These have been considered as relevant since they affect the users’ perception of the places in a different way. The research points out how the IS method can be complementary to the GIS-based detection method to collect more detailed geo-information about the places, but also a very powerful tool to catch geo-information by the street-level imagery, in the territories where no thematic geospatial data are available.
Semantic Mapping of Architectural Heritage via Artificial Intelligence and H-BIM
Starting from the virtual photogrammetric 3D reconstruction, this work proposes a classification method, based on Artificial Intelligence, allowing to semi-automatically characterize the digital models of existing architectural heritage in terms of material mapping and/or decay condition. The obtained data, once classified, is used and transferred in BIM environment, so to favor the construction of in-formative models rich in analytical content. The proposed approach is described with reference to the significant case study of the Chiesa del Carmine in Pisa, for the study and restitution of the liturgical and decorative apparatus, as part of a large-scale research project, still underway, on the reconstruc-tion of the tramezzo screens for the churches of the Mendicant orders.
Point Cloud Segmentation for Scanto BIM: Review of Related Tecniques
The creation of as-built BIM models sees in the scan to BIM modeling one of the most time-consuming activities. Scan to BIM modeling refers to the creation of BIM objects from information derived from point clouds acquired through laser scans or photogrammetric techniques. Numerous studies have been conducted in recent years to identify automation or semi-automation procedures for the scan to BIM modeling process, which consists of different aspects: the recognition of objects within the scene, the modeling of their geometry and the recognition of the relationships between them. The present work aims to analyze actual trends in the automation of scan to BIM activities, highlighting the most used approaches and methodologies currently presented in order to provide a key to un-derstanding the development of a theme still at the dawn of its expression.
Supervised Classification Approach for the Estimation of Degradation
The study presents an innovative approach to classify geomaterials using supervised classification methods from orthophotos derived from UAV (Unmanned Aerial Vehicle) and photogrammetric processing. The case study examined is the Ponte Rotto, dating back to 20 BC, which in antiquity allowed the Appian Way to cross the Calore River – between the provinces of Avellino and Beneven-to – to continue towards the port of Brindisi. In previous studies, experts on geomaterial diagnosis estimated – from aerophotogrammetric orthophotos generated for both bridge elevations – the geo-materials and quantities used for the construction of the monument and an overview of the state of conservation of the monument studied. Orthophotos of facades were imported into CAD software and used as the basis for – according to a manual process – the mapping of the materials. The work presents the results according to automatic Machine Learning clustering from the same orthophotos to identify geomaterials.
Deep Semantic Segmentation of Cultural Built Heritage Point Clouds: Current Results, Challenges and Trends
In the digital Cultural Heritage domain, the ever-increasing availability of 3D point clouds provides the opportunity to rapidly generate detailed 3D scenes to support the restoration, conservation, main-tenance and safeguarding activities of built heritage. The semantic enrichment of these point clouds could support the automatization of the scan-to-BIM processes. In this framework, the use of Artificial Intelligence techniques for the automatic recognition of architectural elements from point clouds can thus provide valuable support. The described methodology allows increasing the Level of Detail in the semantic segmentation of built heritage point clouds compared to the current state-of-the-art through deep neural networks. The main outcome is therefore the first application of DL framework for CH point clouds, with the subsequent implementation of the selected neural network (the DGCNN) for the semantic segmen-tation task. These results also permit to evaluate the pros and cons of this approach, along with future challenges and trend.
A Proposal for Masonry Bridge Health Assessment Using AI and Semantics
Masonry railway bridges represent a historical built heritage to be preserved. This contribution pro-poses a new methodological approach for health assessment of masonry railway bridges based on the definition of image–based and AI–driven survey protocols useful for the creation of semi–automated H–BIM models. To do this, a heuristic approach is required with the integrated combination of tech-niques and methodologies belonging to different fields. As case studies the masonry bridges of the sicilian Circumetnea railway are chosen.
Machine Learning for Cultural Heritage Classification
Cultural Heritage (CH) assets may be defined as integrated spatial systems composed of inter-connected shapes. The classification and organization of geometries within a hierarchical system are functional to their correct interpretation, which is often performed using 3D point clouds. The recurring shapes recognition becomes a crucial activity, nowadays accelerated by Machine Learning (ML) procedures able to associate semantic meaning to geometric data. An interdisciplinary research team [1] has developed a ML supervised approach, tested on the Milan Cathedral and Pomposa Abbey datasets, which presents an innovative multi–level and multi–resolution classification (MLMR) process. The methodology improves the learning activity and optimizes the 3D classification by a hierarchical concept.
From AI to H–BIM: New Interpretative Scenarios in Data Processing
The paper results from preliminary research experiences focused on the use of Artificial Intelligence as a tool for processing the large amount of data that can be obtained from digitisation processes ap-plied to the Architectural Heritage. The new interpretative scenarios will be outlined starting, on one hand, from a series of consolidated experiences in the field of three–dimensional survey, modelling and semantic enrichment, and, on the other, from the use of Augmented Reality tools for the fruition of the Heritage itself.The research aims to further investigate the great potential of processing point cloud models using Artificial Intelligence, to extrapolate, from the digitized data, information levels that go beyond shapes, offering better integration within the Building Information Modeling environment.
Automated Modelling of Masonry Walls: a ML and AR Approach
A methodology for the automated delineation of brick masonries from images to a vector repre-sentation is discussed in this paper. Python environment is chosen for the coding activity in order to provide automation to the process. Edge detection and vector delineation of brick joints are followed by a phase of brick clustering for masonry classification. The implementation of the process is tested on a video sequence to simulate an augmented reality application for masonry detection.
Deep Learning for Point Clouds Classification in the Ducal Palace at Urbino
Starting from a multi–scalar and multi–dimensional survey, most interdisciplinary researches, based on representation, are becoming a tool for dialogue between the new trends of Artificial Intelligence (AI) and the most compelling needs of our CH. The approach here proposed stems from the desire to understand how much of the skills useful in architecture analysing and modelling can be made available to the “machine”, with the goal to accelerate cognitive or management processes. Some HBIM models, as an existing digital heritage, were used to obtain the semantic intelligence. From this specialised intelligence comes a cyclical path which, through AI, transforms this knowledge into new forms of collective intelligence, at the service of the heritage. The paper presents a research that brings very promising results for the segmentation of point clouds and the facilitation of ScanToHBIM approaches, made possible by the large amount of data acquired on the Ducal Palace of Urbino.