Advanced Geomatic Techniques for the Built Heritage: Data Processing, Interpretation and Knowledge Management

A special issue of Geomatics (ISSN 2673-7418).

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 11518

Special Issue Editors


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Guest Editor

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Guest Editor
Department of Civil, Building and Architectural Engineering (DICEA) Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
Interests: archaeology; photogrammetry; data processing; HBIM; ontologies; knowledge management
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Guest Editor
Department of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, Cordo Duca degli Abruzzi 24, 10129 Torino, Italy
Interests: GIS; webGIS; spatial database; BIM/HBIM; geographical standards; architectural and built heritage standards; artificial intelligence; explainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geomatics is a discipline that deals with the automated processing and management of complex 2D or 3D information. It is defined as a multidisciplinary, systemic, and integrated approach that allows the collection, storage, integration, modelling, and analysis of spatially georeferenced data from several sources, with well-defined accuracy characteristics and continuity, in a digital format. Nowadays, the processing of large amounts of data and information in an interdisciplinary and interoperable way relies on a growing variety of tools, together with data collection and processing methods. Furthermore, as in many other fields, computation and interpretation are now regarded as the primary drivers of innovation. Analysis tasks can be performed at a regional level thanks to the use of high-resolution images from satellite or aerial images; inferring information is possible through land-usage classification, and the shape can be described using ranging techniques such as LiDAR and radar pulse. The possibilities offered by new acquisition devices for dealing with architectural-scale complex objects are numerous. Low-cost equipment (cameras, small drones, depth sensors, and so on) are capable of accomplishing reconstruction tasks. Thus, since we live in well-connected digital world, geomatics can play a new role in research and development involving geospatial and related information leading to entrepreneurial opportunities.

Among others, the Built Heritage is the domain where geomatics plays a pivotal role. From a small scale (territory, landscape, historic villages) to a bigger one (architecture, archaeology, historic buildings), geomatics acts as the connection between the data collection stage and the final objective of any project. In more depth, geomatics give indisputable added value for the tasks of data collection, modelling, conservation, preservation, management and visualization. Recent technological advances (from both hardware and software sides) have made the integration of this technique the most straightforward way to embrace all the needs of a delicate domain such as the Built Heritage. This Special Issue aims to fill the gap between tradition and innovation, looking for basic and applied research where well-established methods and new ones are able to support each other to improve the body of knowledge of geomatics in the Built Heritage domain. Therefore, this Special Issue will collect papers related to new trends, best practices, tendences in geospatial technologies and processing methodologies for BH (Built Heritage) sites and scenarios.

We would like to invite you to contribute your recent research, experimental work, reviews and/or case studies, from the acquisition to conservation of BH contributions, covering, but not limited to, the following topics:

  • Mobile mapping systems and co-registration methods in the field of archaeology and Built Heritage. Special attention will be given to algorithm optimization and multi-source data integration.
  • AI-driven solutions for the interpretation of complex and sparse data. Semantic segmentation and classification will be, in the future, the key tasks where Artificial Intelligence will help the Scan-To-BIM process.
  • Knowledge representation and the management of complex 3D models are nowadays performed with the aid of BIM tools for Built Heritage. We look for papers on semantic ontologies, new approaches to information management, data enrichment and BIM interoperability and data exchange with other information techniques.
  • eXtended Reality and Geo-Visualization are gaining more and more importance in the field of Built Heritage dissemination and valorization. New approaches and integrated methodologies of geospatial visualization are important and relevant in this field.

Dr. Roberto Pierdicca
Dr. Francesco Di Stefano
Dr. Francesca Matrone
Guest Editors

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Keywords

  • Mobile Mapping systems and SLAM technology for the built heritage
  • data and sensor integration in BH (integration, registration, algorithms)
  • point cloud processing in BH (filtering, segmentation, classification, modelling, co-registration)
  • semantic classification of point clouds in BH
  • Historic Building Information Modelling (HBIM)
  • Virtual, Augmented and Mixed Reality (VR/AR/MR) in CH
  • semantic ontologies and interoperability among information techniques

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Published Papers (2 papers)

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Research

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28 pages, 24364 KiB  
Article
Quality Assessment of DJI Zenmuse L1 and P1 LiDAR and Photogrammetric Systems: Metric and Statistics Analysis with the Integration of Trimble SX10 Data
by Filippo Diara and Marco Roggero
Geomatics 2022, 2(3), 254-281; https://doi.org/10.3390/geomatics2030015 - 14 Jul 2022
Cited by 14 | Viewed by 6543
Abstract
This manuscript focuses on a quality assessment of DJI’s new sensors: the Zenmuse L1 and P1, which are LiDAR and photographic payload sensors, respectively, for UAVs/UASs. In particular, metric and statistical analyses aim to evaluate the data obtained from different 3D survey instruments. [...] Read more.
This manuscript focuses on a quality assessment of DJI’s new sensors: the Zenmuse L1 and P1, which are LiDAR and photographic payload sensors, respectively, for UAVs/UASs. In particular, metric and statistical analyses aim to evaluate the data obtained from different 3D survey instruments. Furthermore, we compared these sensors with TLS data derived from a Trimble SX10 scanning station. The integration of LiDAR and photogrammetric data was then performed and tested inside a complex architectural context, the medieval Frinco Castle (AT-Italy). Point clouds obtained from aerial and terrestrial instruments were analysed and compared using specific tools to calculate variance/distance between points and cloud alignment (via the ICP algorithm), as well as to perform qualitative estimations (especially roughness analysis). The medieval castle proved crucial for the purpose of analysing different metric data of an extremely complex architecture and achieving more accurate results. The collected dataset and performed analyses are now essential information for the consolidation and restoration programme. Full article
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Review

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19 pages, 1067 KiB  
Review
Remote Sensing Image Scene Classification: Advances and Open Challenges
by Ronald Tombe and Serestina Viriri
Geomatics 2023, 3(1), 137-155; https://doi.org/10.3390/geomatics3010007 - 4 Feb 2023
Cited by 3 | Viewed by 3457
Abstract
Deep learning approaches are gaining popularity in image feature analysis and in attaining state-of-the-art performances in scene classification of remote sensing imagery. This article presents a comprehensive review of the developments of various computer vision methods in remote sensing. There is currently an [...] Read more.
Deep learning approaches are gaining popularity in image feature analysis and in attaining state-of-the-art performances in scene classification of remote sensing imagery. This article presents a comprehensive review of the developments of various computer vision methods in remote sensing. There is currently an increase of remote sensing datasets with diverse scene semantics; this renders computer vision methods challenging to characterize the scene images for accurate scene classification effectively. This paper presents technology breakthroughs in deep learning and discusses their artificial intelligence open-source software implementation framework capabilities. Further, this paper discusses the open gaps/opportunities that need to be addressed by remote sensing communities. Full article
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