Topics Webinar | EO&GEO Series: New Perspectives on 3D Point Clouds in the Heritage Field
the chair
Topics Webinar
EO&GEO Series: New Perspectives on 3D Point Clouds in the Heritage Field
It is my great pleasure to introduce this webinar, New Perspectives on 3D Point Clouds in the Heritage Field
In recent years, the integration of artificial intelligence (AI) with advanced 3D documentation techniques has opened exciting opportunities for cultural heritage research and conservation. Semantic segmentation methods, empowered by machine and deep learning, now allow us to classify and interpret 3D point cloud, enabling automated recognition of architectural elements.
Equally important is the application of AI-driven workflows for damage detection and structural assessment, where 3D point cloud analysis supports the early identification of damage and the monitoring of heritage assets over time.
Both advances not only deepen our understanding of the building but also contribute to the comprehensive development of Heritage Building Information Modeling (HBIM) models, fostering the adoption of this interoperable approach.
This webinar gathers cutting-edge research and innovative case studies that demonstrate how these technologies are reshaping documentation, conservation planning, and heritage interpretation. I am deeply grateful to the speakers for their contributions, which collectively provide a forward-looking vision for the digital future of heritage preservation.
I hope this webinar will inspire new methodologies, interdisciplinary collaborations, and the continued integration of 3D point cloud technologies into cultural heritage workflows.
Prof. Dr. Luis Javier Sánchez Aparicio
Chair of the Special Issue
Department of Construction and Building Technology
Technical School of Architecture. Polytechnic School of Madrid
Spain
Date: 31 October 2025
Time: 5:00 pm CET | 12:00 am EDT
Webinar ID: 853 5782 4039
Webinar Secretariat: journal.webinar@mdpi.com
Meet the Event Chair
Meet Our Speakers
Prof. Dr. Roberto Pierdicca
Università Politecnica delle Marche, Dipartimento di Ingegneria Civile Edile e dell’ Architettura, Ancona (IT);
Roberto Pierdicca is Associate Professor of Geomatics, he got his PhD in Informatics Engineering in 2017. His main research interest is in GeoAI, Geospatial Reasoning and Geoinformatics, from both methodological and applied point of view, spanning from Cultural Heritage to Environmental Monitoring. He also work in the field of eXtended Reality and Forestry. He is author of more than 200 papers in both International journal and conferences, and professor of several courses in Geomatics and Statistics.
In 2021 he was awarded by the EGU with the outstanding oyung investigator award, in 2023/2024 he was mentioned by Standford among the 2% of most citeed awards and in 2025 he was the winner of the Remote Sensing young investigator award.
Mr. Pablo Sanz Honrado
Institute of Physical and Information Technologies Leonardo Torres Quevedo (ITEFI), Spanish National Research Council (CSIC), Madrid, Spain;
Pablo Sanz graduated in Architecture at the Polytechnic University of Madrid (2023) and is currently a pre-doctoral fellow at CSIC’s ITEFI since 2024, focusing on AI and mechatronics for the structural diagnosis of built heritage. His research explores the segmentation of 3D point clouds of historical constructions, applying AI to damage characterization— the main topic of his PhD at the same university. He has also contributed to the 3D digitization of several heritage structures declared UNESCO World Heritage Sites or Assets of Cultural Interest.
Dr. Andrea di Filippo
Department of Civil Engineering, University of Salerno, Italy;
Andrea di Filippo is a Researcher at the Department of Civil Engineering, University of Salerno, where he also earned a European PhD in “Risk and Sustainability in Civil, Building and Environmental Engineering Systems”. His research focuses on Building Information Modelling (BIM), digital databases, and the digitization and management of the built heritage, particularly within the Campania Region.
He has carried out international research activities at the University of Salamanca (Spain), contributing to EU projects such as HeritageCare and Cultural Heritage Through Time, and has participated in missions funded by the Italian Ministry of Foreign Affairs in South Africa and Colombia, in collaboration with universities and local institutions.
He has taught as an adjunct professor at the University of Salerno, the University of Naples “Federico II”, and San Raffaele Telematic University. He is a member of scientific committees for international conferences and editorial series, and author of over 40 scientific publications in the field of Architectural Drawing (ICAR/17). He holds the National Scientific Qualification as Associate Professor in this field.
Sponsors and Partners
Organizer
In this section, you will find the recordings of this webinar to watch, re-watch and share with your colleagues!
New Perspectives on 3D Point Cloud (Fourth Edition)
edited by Sara Gonizzi Barsanti
Deadline for manuscript submissions: 31 March 2026
Hybrid Denoising Algorithm for Architectural Point Clouds Acquired with SLAM Systems
by Antonella Ambrosino, Alessandro Di Benedetto and Margherita Fiani
Remote Sens. 2024, 16(23), 4559; https://doi.org/10.3390/rs16234559
Comparison of NeRF-based methods for point cloud reconstruction for small-sized archaeological artifactsby Miguel Ángel Maté-González, Roy Yali, Jesús Rodríguez-Hernández, Enrique González-González and Julián Aguirre de Mata
Remote Sens. 2025, 17(14), 2535; https://doi.org/10.3390/rs17142535
Scan-to-HBIM-to-VR: An Integrated Approach for the Documentation of an Industrial Archaeology Building
by Maria Alessandra Tini, Anna Forte, Valentina Alena Girelli, Alessandro Lambertini, Domenico Simone Roggio, Gabriele Bitelli and Luca Vittuari
Remote Sens. 2024, 16(15), 2859; https://doi.org/10.3390/rs16152859
3D Reconstruction of Ancient Buildings using UAV Images and Neural Radiation Field with Depth Supervision
by Yingwei Ge, Bingxuan Guo, Peishuai Zha, San Jiang, Ziyu Jiang and Demin Li
Remote Sens. 2024, 16(3), 473; https://doi.org/10.3390/rs16030473