The reconstruction of 3D geometries starting from reality-based data is challenging and time-consuming due to the difficulties involved in modeling existing structures and the complex nature of built heritage. This paper presents a methodological approach for the automated segmentation and classification of surveying outputs to improve the interpretation and building information modeling from laser scanning and photogrammetric data. The research focused on the surveying of reticular, space grid structures of the late 19th–20th–21st centuries, as part of our architectural heritage, which might require monitoring maintenance activities, and relied on artificial intelligence (machine learning and deep learning) for: (i) the classification of 3D architectural components at multiple levels of detail and (ii) automated masking in standard photogrammetric processing. Focusing on the case study of the grid structure in steel named La Vela in Bologna, the work raises many critical issues in space grid structures in terms of data accuracy, geometric and spatial complexity, semantic classification, and component recognition.

Machine Learning and Deep Learning for the Built Heritage Analysis: Laser Scanning and UAV-Based Surveying Applications on a Complex Spatial Grid Structure / Billi, Dario; Croce, Valeria; Giorgio Bevilacqua, Marco; Caroti, Gabriella; Pasqualetti, Agnese; Piemonte, Andrea; Russo, Michele. - In: REMOTE SENSING. - ISSN 2072-4292. - 15:8(2023), pp. 1-34. [10.3390/rs15081961]

Machine Learning and Deep Learning for the Built Heritage Analysis: Laser Scanning and UAV-Based Surveying Applications on a Complex Spatial Grid Structure

Michele Russo
2023

Abstract

The reconstruction of 3D geometries starting from reality-based data is challenging and time-consuming due to the difficulties involved in modeling existing structures and the complex nature of built heritage. This paper presents a methodological approach for the automated segmentation and classification of surveying outputs to improve the interpretation and building information modeling from laser scanning and photogrammetric data. The research focused on the surveying of reticular, space grid structures of the late 19th–20th–21st centuries, as part of our architectural heritage, which might require monitoring maintenance activities, and relied on artificial intelligence (machine learning and deep learning) for: (i) the classification of 3D architectural components at multiple levels of detail and (ii) automated masking in standard photogrammetric processing. Focusing on the case study of the grid structure in steel named La Vela in Bologna, the work raises many critical issues in space grid structures in terms of data accuracy, geometric and spatial complexity, semantic classification, and component recognition.
2023
3D surveying; digital heritage; artificial intelligence; machine learning; classification; point cloud; reticular grid structures; La Vela; civil infrastructures; monitoring
01 Pubblicazione su rivista::01a Articolo in rivista
Machine Learning and Deep Learning for the Built Heritage Analysis: Laser Scanning and UAV-Based Surveying Applications on a Complex Spatial Grid Structure / Billi, Dario; Croce, Valeria; Giorgio Bevilacqua, Marco; Caroti, Gabriella; Pasqualetti, Agnese; Piemonte, Andrea; Russo, Michele. - In: REMOTE SENSING. - ISSN 2072-4292. - 15:8(2023), pp. 1-34. [10.3390/rs15081961]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1677860
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