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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 Remote Sensing.

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. Infrastructures is an international peer-reviewed open access monthly 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 1800 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.

Related Special Issue

Published Papers (1 paper)

Research

31 pages, 7483 KiB

Open AccessArticle

Discrete and Distributed Error Assessment of UAS-SfM Point Clouds of Roadways

by Yijun Liao and Richard L. Wood

Infrastructures 2020, 5(10), 87; https://0-doi-org.brum.beds.ac.uk/10.3390/infrastructures5100087 - 18 Oct 2020

Cited by 5 | Viewed by 2400

Abstract

Perishable surveying, mapping, and post-disaster damage data typically require efficient and rapid field collection techniques. Such datasets permit highly detailed site investigation and characterization of civil infrastructure systems. One of the more common methods to collect, preserve, and reconstruct three-dimensional scenes digitally, is the use of an unpiloted aerial system (UAS), commonly known as a drone. Onboard photographic payloads permit scene reconstruction via structure-from-motion (SfM); however, such approaches often require direct site access and survey points for accurate and verified results, which may limit its efficiency. In this paper, the impact of the number and distribution of ground control points within a UAS SfM point cloud is evaluated in terms of error. This study is primarily motivated by the need to understand how the accuracy would vary if site access is not possible or limited. In this paper, the focus is on two remote sensing case studies, including a 0.75 by 0.50-km region of interest that contains a bridge structure, paved and gravel roadways, vegetation with a moderate elevation range of 24 m, and a low-volume gravel road of 1.0 km in length with a modest elevation range of 9 m, which represent two different site geometries. While other studies have focused primarily on the accuracy at discrete locations via checkpoints, this study examines the distributed errors throughout the region of interest via complementary light detection and ranging (lidar) datasets collected at the same time. Moreover, the international roughness index (IRI), a professional roadway surface standard, is quantified to demonstrate the impact of errors on roadway quality parameters. Via quantification and comparison of the differences, guidance is provided on the optimal number of ground control points required for a time-efficient remote UAS survey. Full article

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

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