Special Issue "Heritage 3D Modeling from Remote Sensing Data"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (30 April 2020).

Special Issue Editors

Dr. Pablo Rodríguez-Gonzálvez
SciProfiles
Guest Editor
Prof. Dr. Francesco Fassi
Website
Guest Editor
ABC Department, 3DsurveyGroup, Politecnico di Milano, Via G. Ponzio 31, 20133 Milano, Italy
Interests: photogrammetry; cultural heritage; laser scanning; 3D reconstruction; 3D modeling; BIM; virtual reality

Special Issue Information

Dear Colleagues,

The advancement of image-based sensors and algorithms in recent years has led to changes in work methodologies in different fields, and the area of cultural heritage (CH) is not an exception. The possibilities for CH recording, documentation, and dissemination have increased, whereas 3D modeling techniques have acquired certain maturity in the last decade. However, the creation of reliable 3D CH models from images still present geometric and radiometric difficulties. Moreover, CH analysis involves several disciplines (archeologists, historians, surveyors, etc.), so each of them aims at optimizing different performance metrics. The inherent complexity of CH leads to a combination of sensors and techniques, but their fusion and hybridization is a hot topic in the scientific community. Moreover, the necessity of a complete virtual documentation of the CH element for planning and management tasks requires the consideration of specific modeling algorithms to automate the operation, as well as specific information models, such as Heritage BIM. This escalating demand, along with the need for accurate and dense 3D geospatial information in CH, is the driving force that leads geomatics to propose innovative solutions, ranging from new open source software approaches to the development of artificial intelligence approaches. The aim of the present Special Issue is to cover the relevant topics, trends, and best practices in image-based 3D modeling for CH sites and scenarios, and also to introduce new tendencies in the field.

We would like to invite you to contribute by submitting articles about your recent research, experimental work, reviews, and/or case studies related to the field of image-based modeling. Contributions may be from, but not limited to, the following topics:

  • 3D modeling for CH from images from different geomatics sensors: Terrestrial, drones, airborne, satellite;
  • Latest developments in image-based documentation techniques;
  • 3D modeling of historical heritage;
  • Automation in 3D modeling;
  • Data and sensor integration;
  • Multiscale CH documentation;
  • Semantic classification of point clouds;
  • From point cloud to BIM for CH assets/sites;
  • BIM applied to CH assets/sites;
  • Virtual reality for CH;
  • Open source software approaches;
  • Accuracy and precision assessment of CH modeling;
  • Remote sensing for CH;
  • Mobile mapping for CH documentation.

Dr. Pablo Rodríguez-Gonzálvez
Prof. Dr. Francesco Fassi
Prof. Dr. Fabio Remondino
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Image-based modeling
  • Photogrammetry
  • 3D modeling
  • Data fusion
  • Sensor integration
  • Virtual reality
  • Semantic classification
  • Building information modeling (BIM)
  • Accuracy and precision assessment

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
HBIM for Conservation: A New Proposal for Information Modeling
Remote Sens. 2019, 11(15), 1751; https://doi.org/10.3390/rs11151751 - 25 Jul 2019
Cited by 11
Abstract
Thanks to its capability of archiving and organizing all the information about a building, HBIM (Historical Building Information Modeling) is considered a promising resource for planned conservation of historical assets. However, its usage remains limited and scarcely adopted by the subjects in charge [...] Read more.
Thanks to its capability of archiving and organizing all the information about a building, HBIM (Historical Building Information Modeling) is considered a promising resource for planned conservation of historical assets. However, its usage remains limited and scarcely adopted by the subjects in charge of conservation, mainly because of its rather complex 3D modeling requirements and a lack of shared regulatory references and guidelines as far as semantic data are concerned. In this study, we developed an HBIM methodology to support documentation, management, and planned conservation of historic buildings, with particular focus on non-geometric information: organized and coordinated storage and management of historical data, easy analysis and query, time management, flexibility, user-friendliness, and information sharing. The system is based on a standalone specific-designed database linked to the 3D model of the asset, built with BIM software, and it is highly adaptable to different assets. The database is accessible both with a developed desktop application, which acts as a plug-in for the BIM software, and through a web interface, implemented to ensure data sharing and easy usability by skilled and unskilled users. The paper describes in detail the implemented system, passing by semantic breaking down of the building, database design, as well as system architecture and capabilities. Two case studies, the Cathedral of Parma and Ducal Palace of Mantua (Italy), are then presented to show the results of the system’s application. Full article
(This article belongs to the Special Issue Heritage 3D Modeling from Remote Sensing Data)
Show Figures

Graphical abstract

Open AccessArticle
Clustering of Wall Geometry from Unstructured Point Clouds Using Conditional Random Fields
Remote Sens. 2019, 11(13), 1586; https://doi.org/10.3390/rs11131586 - 04 Jul 2019
Cited by 4
Abstract
The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still subject of ongoing research. A vital step in the process is identifying the observations for each wall object. Given a set of segmented and classified point clouds, the [...] Read more.
The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still subject of ongoing research. A vital step in the process is identifying the observations for each wall object. Given a set of segmented and classified point clouds, the labeled segments should be clustered according to their respective objects. The current processes to perform this task are sensitive to noise, occlusions, and the associativity between faces of neighboring objects. The proper retrieval of the observed geometry is especially important for wall geometry as it forms the basis for further BIM reconstruction. In this work, a method is presented to automatically group wall segments derived from point clouds according to the proper walls of a building. More specifically, a Conditional Random Field is employed that evaluates the context of each wall segment in order to determine which wall it belongs to. First, a set of classified planar primitives is obtained through algorithms developed in prior work. Next, both local and contextual features are extracted based on the nearest neighbors and a number of seeds that are heuristically determined. The final wall clusters are then computed by decoding the graph. The method is tested on our own data as well as the 2D-3D-Semantics (2D-3D-S) benchmark data of Stanford. Compared to a conventional region growing method, the proposed method reduces the rate of false positives, resulting in better wall clusters. Overall, the method computes a more balanced clustering of the observations. A key advantage of the proposed method is its capability to deal with wall geometry in complex configurations in multi-storey buildings opposed to the presented methods in current literature. Full article
(This article belongs to the Special Issue Heritage 3D Modeling from Remote Sensing Data)
Show Figures

Figure 1

Open AccessFeature PaperArticle
Classification of 3D Digital Heritage
Remote Sens. 2019, 11(7), 847; https://doi.org/10.3390/rs11070847 - 08 Apr 2019
Cited by 10
Abstract
In recent years, the use of 3D models in cultural and archaeological heritage for documentation and dissemination purposes is increasing. The association of heterogeneous information to 3D data by means of automated segmentation and classification methods can help to characterize, describe and better [...] Read more.
In recent years, the use of 3D models in cultural and archaeological heritage for documentation and dissemination purposes is increasing. The association of heterogeneous information to 3D data by means of automated segmentation and classification methods can help to characterize, describe and better interpret the object under study. Indeed, the high complexity of 3D data along with the large diversity of heritage assets themselves have constituted segmentation and classification methods as currently active research topics. Although machine learning methods brought great progress in this respect, few advances have been developed in relation to cultural heritage 3D data. Starting from the existing literature, this paper aims to develop, explore and validate reliable and efficient automated procedures for the classification of 3D data (point clouds or polygonal mesh models) of heritage scenarios. In more detail, the proposed solution works on 2D data (“texture-based” approach) or directly on the 3D data (“geometry-based approach) with supervised or unsupervised machine learning strategies. The method was applied and validated on four different archaeological/architectural scenarios. Experimental results demonstrate that the proposed approach is reliable and replicable and it is effective for restoration and documentation purposes, providing metric information e.g. of damaged areas to be restored. Full article
(This article belongs to the Special Issue Heritage 3D Modeling from Remote Sensing Data)
Show Figures

Graphical abstract

Back to TopTop