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Special Issue "Integrating Remote Sensing Data for Transportation Asset Management"

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

Deadline for manuscript submissions: closed (15 January 2023) | Viewed by 3602

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Special Issue Editors

Prof. Dr. Senzhang Wang

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Guest Editor

School of Computer Science and Engineering, Central South University, Changsha 410083, China
Interests: spatio-temporal data mining; big data; graph data mining

Prof. Dr. Zhenwei Shi

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Guest Editor

Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China
Interests: remote sensing image processing and analysis; computer vision; pattern recognition; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Yisheng Lv

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Guest Editor

Institute of Automation, Chinese Academy of Science, Beijing 100190, China
Interests: ITS; deep learning; machine learning; traffic big data

Dr. Renhe Jiang

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Guest Editor

Information Technology Center, The University of Tokyo,Tokyo 113-0032, Japan
Interests: human mobility; spatio-temporal data; deep learning; ubiquitous computing

Special Issue Information

Dear Colleagues,

In the last decades, due to the development of global positioning and inertial units (GNSS/IMU), there emerges many technologies to acquire remote sensing data. Airborne LiDAR has been prevalent in 1970’s and 1980’s, besides, other alternative or complementary technologies has been also present at different scales, including airborne/shuttle/satellite radar, terrain laser scanning or photogrammetry from either photogrammetric or consumer grade cameras later. These remote sensing data acquired by different sensors and platforms exist in many forms, such as 3D point clouds and images, and they contain rich information about certain geographic locations, climate and environment. Particularly, with the use of these multi-sourced data, a lot of research and development could take place to improve the standard of the operation, like traffic planning, environment impact assessment, hazard and disaster response, infrastructure management, traffic assessment, and homeland security planning.

Remote sensing is an innovative science and technology that is aiding in numerous modes of transportation. Almost every aspect of transportation can benefit from utilizing imagery and data. Remote sensing provides unique ability to detect changes in our transportation system on a real-time bias. Integrate these multi-sourced and heterogeneous remote sensing data and extract useful information from them for specific tasks, such as transportation asset management and traffic flow prediction will make the lives of people much easier and convenient.

This Special Issue aims at studies covering the use of remote sensing data for transportation asset management. Topics may cover anything from the aspect of spatial temporal mining, remote sensing data integration and transportation asset planning and management. Hence, multi-sourced data integration (e.g. multispectral, hyper spectral, and thermal) and heterogeneous data integration (e.g. images and 3D point clouds) approaches or studies focus on transportation planning, congestion relief, asset management, among other issues, are welcome.

Articles may address, but are not limited, to the following topics:

Terrain analysis
Traffic flow estimation and prediction
Traffic data integration
Transportation anomaly detection
Trafficability analysis
Transportation asset planning and management
Traffic accident detection
Traffic congestion relief
Transportation infrastructure
Rural road discovery and management
Availability of parking lots estimation
Transportation network update
Travel time estimation

Prof. Dr. Senzhang Wang
Prof. Dr. Zhenwei Shi
Dr. Yisheng Lv
Dr. Renhe Jiang
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 2500 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

remote sensing
traffic data integration
anomaly detection
trafficability asset management
traffic flow prediction
spectral and structural data fusion
heterogeneous data fusion

Published Papers (4 papers)

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Research

Open AccessArticle

People Flow Trend Estimation Approach and Quantitative Explanation Based on the Scene Level Deep Learning of Street View Images

and

Remote Sens. 2023, 15(5), 1362; https://doi.org/10.3390/rs15051362 - 28 Feb 2023

Viewed by 383

Abstract

People flow trend estimation is crucial to traffic and urban safety planning and management. However, owing to privacy concerns, the collection of individual location data for people flow statistical analysis is difficult; thus, an alternative approach is urgently needed. Furthermore, the trend in people flow is reflected in streetscape factors, yet the relationship between them remains unclear in the existing literature. To address this, we propose an end-to-end deep-learning approach that combines street view images and human subjective score of each street view. For a more detailed people flow study, estimation and analysis were implemented using different time and movement patterns. Consequently, we achieved a 78% accuracy on the test set. We also implemented the gradient-weighted class activation mapping deep learning visualization and L1 based statistical methods and proposed a quantitative analysis approach to understand the land scape elements and subjective feeling of street view and to identify the effective elements for the people flow estimation based on a gradient impact method. In summary, this study provides a novel end-to-end people flow trend estimation approach and sheds light on the relationship between streetscape, human subjective feeling, and people flow trend, thereby making an important contribution to the evaluation of existing urban development. Full article

(This article belongs to the Special Issue Integrating Remote Sensing Data for Transportation Asset Management)

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Open AccessArticle

SW-GAN: Road Extraction from Remote Sensing Imagery Using Semi-Weakly Supervised Adversarial Learning

and

Remote Sens. 2022, 14(17), 4145; https://doi.org/10.3390/rs14174145 - 23 Aug 2022

Cited by 3 | Viewed by 811

Abstract

Road networks play a fundamental role in our daily life. It is of importance to extract the road structure in a timely and precise manner with the rapid evolution of urban road structure. Recently, road network extraction using deep learning has become an effective and popular method. The main shortcoming of the road extraction using deep learning methods lies in the fact that there is a need for a large amount of training datasets. Additionally, the datasets need to be elaborately annotated, which is usually labor-intensive and time-consuming; thus, lots of weak annotations (such as the centerline from OpenStreetMap) have accumulated over the past a few decades. To make full use of the weak annotations, we propose a novel semi-weakly supervised method based on adversarial learning to extract road networks from remote sensing imagery. Our method uses a small set of pixel-wise annotated data and a large amount of weakly annotated data for training. The experimental results show that the proposed approach can achieve a maintained performance compared with the methods that use a large number of full pixel-wise annotations while using less fully annotated data. Full article

(This article belongs to the Special Issue Integrating Remote Sensing Data for Transportation Asset Management)

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Open AccessArticle

Examining Impact of Speed Recommendation Algorithm Operating in Autonomous Road Signs on Minimum Distance between Vehicles

and

Remote Sens. 2022, 14(12), 2803; https://doi.org/10.3390/rs14122803 - 10 Jun 2022

Cited by 2 | Viewed by 762

Abstract

An approach to a new kind of recommendation system design that suggests safe speed on the road is presented. Real data obtained on roads were used for the simulations. As part of a project related to autonomous road sign development, a number of measurements were carried out on both local roads and expressways. A speed recommendation model was created based on gathered traffic data employing the traffic simulator. Depending on the traffic volume and atmospheric conditions prevailing on the road, as well as the surface conditions, the proposed system recommends the safe speed for passing vehicles by influencing the distance from the preceding vehicle to prevent collisions. The observed effect of the system application was an increase in the minimal distance between vehicles in most simulations. Full article

(This article belongs to the Special Issue Integrating Remote Sensing Data for Transportation Asset Management)

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Open AccessArticle

Verification of Satellite Railway Track Position Measurements Making Use of Standard Coordinate Determination Techniques

Karolina Makowska-Jarosik

Michał Michna

and

Tadeusz Widerski

Remote Sens. 2022, 14(8), 1855; https://doi.org/10.3390/rs14081855 - 12 Apr 2022

Cited by 3 | Viewed by 912

Abstract

The article presents the results of satellite railway track position measurements performed by a multidisciplinary research team, the members of which represented Gdansk University of Technology and Gdynia Maritime University. Measuring methods are described which were used for reconstructing the railway track axis position and diagnosing railway track geometry deformations. As well as that, the description of the novel method developed by the authors to perform mobile GNSS measurements is included. The reported research aimed at assessing the uncertainty of railway track axis reconstruction making use of the dynamic GNSS method. To assess the applicability of this method, the obtained results were compared with those from the stationary measurement method used in railway business. The data used for comparison was recorded on the same railway track section during several measurement campaigns. In these campaigns, different types of GNSSs with different position recording frequencies (1–100 Hz) were used at different measurement speeds (5–70 km/h). The performed analysis has shown that the accuracy of railway track axis reconstruction making use of mobile GNSS measurements is sufficient for using this methodology in railway business. Full article

(This article belongs to the Special Issue Integrating Remote Sensing Data for Transportation Asset Management)

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