Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Advances in Remote Sensing and Digital Twin Technologies for Transportation...
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advances in Remote Sensing and Digital Twin Technologies for Transportation Infrastructure

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

Deadline for manuscript submissions: 15 April 2025 | Viewed by 1419

Special Issue Editors

Dr. Zhen Liu

E-Mail
Guest Editor
Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA 16802, USA
Interests: non-destructive testing; ground-penetrating radar; deep learning; remote sensing technologies; digital twin
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xingyu Gu

E-Mail Website
Guest Editor
School of Transportation, Southeast University, Nanjing 211189, China
Interests: NDT technologies; structural health monitoring; advanced sensors; remote sensing; green materials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the ever-dynamic and vast expanse of transportation activities that unfold incessantly across diverse modalities—including roadways, railways, airports, and waterways—the accessibility, identifiability, and certainty of environmental factors play pivotal roles. These elements not only facilitate the seamless execution of transportation endeavors but also fortify the security framework encompassing them. Fortuitously, the recent surge in the advancement of remote sensing technologies heralds a new era of promising possibilities and competitive advantages across a broad spectrum of transportation applications. These technologies (SAR, InSAR, LiDAR, UAV, GPR), in synergy with meticulously crafted algorithms, have markedly elevated the quality and standards of task-oriented outcomes.

Concurrently, the advent of digital twin technologies (BIM, GIS, IoT) has introduced the creation of dynamic, virtual counterparts to physical transportation infrastructures. These virtual models are perpetually refreshed with real-time data, thereby enabling the simulation, testing, and optimization of infrastructure performances across a multitude of scenarios, all without exerting any direct impact on the physical systems themselves.

This Special Issue is dedicated to exploring the forefront of research and development in the amalgamation of remote sensing technologies and digital twin concepts within the transportation infrastructure domain. It presents a collection of scholarly articles and research papers that probe into the transformative impacts that these advanced technological solutions exert on the monitoring, management, and maintenance of transportation networks, encompassing roads, bridges, railways, and airports. By spotlighting recent innovations and prospective trajectories, this issue aims to significantly enrich the dialogue surrounding intelligent infrastructure management and lay the groundwork for the emergence of more resilient and adaptable transportation systems. Areas of interest include, but are not necessarily restricted to the following:

  • Transportation infrastructure monitoring using remote sensing techniques.
  • The real-time access and integrated fusion of multi-source heterogeneous data in complex traffic scenarios.
  • The extraction of road traffic network information based on remote sensing influence.
  • Pavement distress measurements and evaluations using AI methods.
  • Multi-modal remote sensing for transportation infrastructure inspection.
  • The risk analysis of transportation infrastructure using InSAR technology.
  • Innovative transportation infrastructure promoting the development of autonomous driving and intelligent vehicle-road collaboration.
  • Digital twin technology in innovative applications across global transportation scenarios
  • Green and low-carbon operation and management technologies for transportation infrastructure.

Dr. Zhen Liu
Prof. Dr. Xingyu Gu
Guest Editors

Name: Bingyan Cui
Guest Editor Assistant
Email: [email protected]
Affiliation: Department of Civil and Environmental Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
Website: https://www.scopus.com/authid/detail.uri?authorId=57204906428
Interests: road monitoring; AI applications; road pavement distress assessment; remote sensing for natural disaster assessments

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

  • transportation infrastructure
  • road extraction
  • traffic network
  • transportation infrastructure
  • remote sensing
  • InSAR
  • AI
  • digital twin

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

37 pages, 6394 KiB  
Article
Insights into the Effects of Tile Size and Tile Overlap Levels on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography
by Calimanut-Ionut Cira, Miguel-Ángel Manso-Callejo, Ramon Alcarria, Teresa Iturrioz and José-Juan Arranz-Justel
Remote Sens. 2024, 16(16), 2954; https://doi.org/10.3390/rs16162954 - 12 Aug 2024
Viewed by 1043
Abstract
Studies addressing the supervised extraction of geospatial elements from aerial imagery with semantic segmentation operations (including road surface areas) commonly feature tile sizes varying from 256 × 256 pixels to 1024 × 1024 pixels with no overlap. Relevant geo-computing works in the field [...] Read more.
Studies addressing the supervised extraction of geospatial elements from aerial imagery with semantic segmentation operations (including road surface areas) commonly feature tile sizes varying from 256 × 256 pixels to 1024 × 1024 pixels with no overlap. Relevant geo-computing works in the field often comment on prediction errors that could be attributed to the effect of tile size (number of pixels or the amount of information in the processed image) or to the overlap levels between adjacent image tiles (caused by the absence of continuity information near the borders). This study provides further insights into the impact of tile overlaps and tile sizes on the performance of deep learning (DL) models trained for road extraction. In this work, three semantic segmentation architectures were trained on data from the SROADEX dataset (orthoimages and their binary road masks) that contains approximately 700 million pixels of the positive “Road” class for the road surface area extraction task. First, a statistical analysis is conducted on the performance metrics achieved on unseen testing data featuring around 18 million pixels of the positive class. The goal of this analysis was to study the difference in mean performance and the main and interaction effects of the fixed factors on the dependent variables. The statistical tests proved that the impact on performance was significant for the main effects and for the two-way interaction between tile size and tile overlap and between tile size and DL architecture, at a level of significance of 0.05. We provide further insights and trends in the predictions of the extensive qualitative analysis carried out with the predictions of the best models at each tile size. The results indicate that training the DL models on larger tile sizes with a small percentage of overlap delivers better road representations and that testing different combinations of model and tile sizes can help achieve a better extraction performance. Full article
(This article belongs to the Special Issue Advances in Remote Sensing and Digital Twin Technologies for Transportation Infrastructure)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop