Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Browser

Remote Sensing and Infrastructure Information Models: Methods, Applications and Smart Management of Infrastructure Data

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Related Special Issue
Published Papers

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

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 11179

Share This Special Issue

Special Issue Editors

Dr. Mario Soilán

E-Mail Website
Guest Editor

Department of Cartographic and Land Engineering, University of Salamanca, Higher Polytechnic School of Avila, Hornos Caleros 50, 05003 Avila, Spain
Interests: laser scanning; Infrastructure monitoring; BIM; IIM; IFC
Special Issues, Collections and Topics in MDPI journals

Dr. Ana Sánchez-Rodríguez

E-Mail Website
Co-Guest Editor

Department of Materials Engineering, Applied Mechanics and Construction, School of Industrial Engineering, University of Vigo, 36310 Vigo, Spain
Interests: laser scanning; Infrastructure monitoring; BIM; IIM; IFC
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During the last decade, different remote sensing technologies have proven their capabilities to effectively extract both geometric and semantic information from the road and railway transport infrastructures. Nowadays, they are widely used in many civil engineering applications. Alongside with the research and development on remote sensing data processing, there is an increasing need of more efficient standardization, management and interoperability of the infrastructure data. In this respect, Infrastructure Information Modeling (IIM) is slowly gaining visibility as an analogy for Building Information Modeling (BIM), that is, a data management process of an infrastructure from its design phase and during its entire life cycle.

While remote sensing technologies are able to efficiently collect data at large scale, IIM should allow data interoperability for a standardized access to asset management databases, improving the efficiency of the information management, maintenance works, and risk assessment of the built environment of the infrastructure. Therefore, there exists a clear symbiosis among both concepts, with remote sensing (i.e. laser scanning, satellite sensors) collecting the as-built data needed for an efficient implementation of an information model of the infrastructure.

This Special Issue aims to collect new knowledge on three main aspects:

Remote sensing data processing methodologies and applications with a special focus on road and railway infrastructure modeling. This aspect could cover, but is not limited to:
- Evaluation of different sensors and technologies for specific infrastructure analysis purposes.
- Automated processing of 3D and 2D remotely sensed infrastructure data, to extract meaningful information for infrastructure models. This may include novel artificial intelligence approaches.
- Big data management, organization and visualization
New Infrastructure Information Modeling approaches, including but not limited to:
- Reviews of current and future trends.
- Digital twins of infrastructure assets
- Applications of existing standards.
Novel approaches that effectively integrate remotely sensed data with IIM, following existing standards.

You may choose our Joint Special Issue in Infrastructures.

Dr. Mario Soilán
Ms. Ana Sánchez-Rodríguez
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 submissions that pass pre-check are 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 2700 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

Infrastructure Information Models
Infrastructure BIM
Point cloud data processing
Satellite data processing
Infrastructure monitoring and maintenance
Digital Twin

Related Special Issue

Remote Sensing and Infrastructure Information Models: Methods, Applications and Smart Management of Infrastructure Data in Infrastructures (1 article)

Published Papers (2 papers)

Download All Papers

Research

30 pages, 7050 KiB

Open AccessArticle

Automatic Point Cloud Semantic Segmentation of Complex Railway Environments

by Daniel Lamas, Mario Soilán, Javier Grandío and Belén Riveiro

Remote Sens. 2021, 13(12), 2332; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122332 - 14 Jun 2021

Cited by 20 | Viewed by 4232

Abstract

The growing development of data digitalisation methods has increased their demand and applications in the transportation infrastructure field. Currently, mobile mapping systems (MMSs) are one of the most popular technologies for the acquisition of infrastructure data, with three-dimensional (3D) point clouds as their main product. In this work, a heuristic-based workflow for semantic segmentation of complex railway environments is presented, in which their most relevant elements are classified, namely, rails, masts, wiring, droppers, traffic lights, and signals. This method takes advantage of existing methodologies in the field for point cloud processing and segmentation, taking into account the geometry and spatial context of each classified element in the railway environment. This method is applied to a 90-kilometre-long railway lane and validated against a manual reference on random sections of the case study data. The results are presented and discussed at the object level, differentiating the type of the element. The indicators F1 scores obtained for each element are superior to 85%, being higher than 99% in rails, the most significant element of the infrastructure. These metrics showcase the quality of the algorithm, which proves that this method is efficient for the classification of long and variable railway sections, and for the assisted labelling of point cloud data for future applications based on training supervised learning models. Full article

(This article belongs to the Special Issue Remote Sensing and Infrastructure Information Models: Methods, Applications and Smart Management of Infrastructure Data)

► Show Figures

Graphical abstract

22 pages, 10783 KiB

Open AccessFeature PaperArticle

3D Point Cloud to BIM: Semi-Automated Framework to Define IFC Alignment Entities from MLS-Acquired LiDAR Data of Highway Roads

by Mario Soilán, Andrés Justo, Ana Sánchez-Rodríguez and Belén Riveiro

Remote Sens. 2020, 12(14), 2301; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12142301 - 17 Jul 2020

Cited by 38 | Viewed by 5874

Abstract

Building information modeling (BIM) is a process that has shown great potential in the building industry, but it has not reached the same level of maturity for transportation infrastructure. There is a standardization need for information exchange and management processes in the infrastructure that integrates BIM and Geographic Information Systems (GIS). Currently, the Industry Foundation Classes standard has harmonized different infrastructures under the Industry Foundation Classes (IFC) 4.3 release. Furthermore, the usage of remote sensing technologies such as laser scanning for infrastructure monitoring is becoming more common. This paper presents a semi-automated framework that takes as input a raw point cloud from a mobile mapping system, and outputs an IFC-compliant file that models the alignment and the centreline of each road lane in a highway road. The point cloud processing methodology is validated for two of its key steps, namely road marking processing and alignment and road line extraction, and a UML diagram is designed for the definition of the alignment entity from the point cloud data. Full article

(This article belongs to the Special Issue Remote Sensing and Infrastructure Information Models: Methods, Applications and Smart Management of Infrastructure Data)

► Show Figures