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Remote Sensing for Intelligent Transportation Systems in Smart Cities

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

Deadline for manuscript submissions: closed (15 April 2023) | Viewed by 6658

Special Issue Editors


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Guest Editor
Big Data and Machine Learning Lab South Ural State, University Chelyabinsk, Chelyabinsk 454080, Russia
Interests: deep learning; medical image analysis; healthcare applications; secret sharing scheme & digital image security
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea
Interests: MAC; routing protocols for next-generation wireless networks; wireless sensor networks; cognitive radio networks; RFID systems; IoT; smart city; deep learning; digital convergence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, the number of research activities related to the utilization of remote sensing technologies towards their implementation in transportation system has increased tremendously. Transportation systems have become a fundamental basis for the economic growth of all nations. Nevertheless, many cities around the world are facing an uncontrolled growth in traffic volume, causing serious problems, such as delays, traffic jams, higher fuel prices, increased CO2 emissions, accidents, emergencies, and the degradation of the quality of life in modern society. Advances in information and communication technologies (ICT) in areas such as hardware, software, and communications have created new opportunities for developing a sustainable, intelligent transportation system. The integration of ICT with the transportation infrastructure will enable a better, safer traveling experience and migration to intelligent transportation systems (ITS), which focus on four fundamental principles: sustainability, integration, safety, and responsiveness. The success of ITS largely depends on the platform used to access, collect, and process accurate data from the environment. Remote sensing (both terrestrial and satellite) has been revealed to be a suitable approach for effectively collecting data on a large scale, with an accuracy level that satisfies the ITS demand. Different advanced technologies have enabled automated modeling, and the interpretation of the data is an interesting topic of remote sensing-based ITS.

Dr. Shankar Kathiresan
Prof. Dr. Seifedine Kadry
Dr. Gyanendra Prasad Joshi
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

  • application of advanced technologies in ITS
  • artificial intelligence for ITS in remote sensing
  • automatic onboard processing techniques for ITS
  • communication technologies for ITS in remote sensing
  • networking resource management for ITS in remote sensing
  • remote sensing based ITS for smart cities
  • research on intelligence computing models for ITS
  • security and privacy in ITS
  • sensing technologies for ITS in remote sensing
  • smart decision support systems for ITS

Published Papers (3 papers)

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Research

17 pages, 1979 KiB  
Article
Anomaly Detection in Pedestrian Walkways for Intelligent Transportation System Using Federated Learning and Harris Hawks Optimizer on Remote Sensing Images
by Manal Abdullah Alohali, Mohammed Aljebreen, Nadhem Nemri, Randa Allafi, Mesfer Al Duhayyim, Mohamed Ibrahim Alsaid, Amani A. Alneil and Azza Elneil Osman
Remote Sens. 2023, 15(12), 3092; https://doi.org/10.3390/rs15123092 - 13 Jun 2023
Cited by 1 | Viewed by 1468
Abstract
Anomaly detection in pedestrian walkways is a vital research area that uses remote sensing, which helps to optimize pedestrian traffic and enhance flow to improve pedestrian safety in intelligent transportation systems (ITS). Engineers and researchers can formulate more potential techniques and tools with [...] Read more.
Anomaly detection in pedestrian walkways is a vital research area that uses remote sensing, which helps to optimize pedestrian traffic and enhance flow to improve pedestrian safety in intelligent transportation systems (ITS). Engineers and researchers can formulate more potential techniques and tools with the power of computer vision (CV) and machine learning (ML) for mitigating potential safety hazards and identifying anomalies (i.e., vehicles) in pedestrian walkways. The real-world challenges of scenes and dynamics of environmental complexity cannot be handled by the conventional offline learning-based vehicle detection method and shallow approach. With recent advances in deep learning (DL) and ML areas, authors have found that the image detection issue ought to be devised as a two-class classification problem. Therefore, this study presents an Anomaly Detection in Pedestrian Walkways for Intelligent Transportation Systems using Federated Learning and Harris Hawks Optimizer (ADPW-FLHHO) algorithm on remote sensing images. The presented ADPW-FLHHO technique focuses on the identification and classification of anomalies, i.e., vehicles in the pedestrian walkways. To accomplish this, the ADPW-FLHHO technique uses the HybridNet model for feature vector generation. In addition, the HHO approach is implemented for the optimal hyperparameter tuning process. For anomaly detection, the ADPW-FLHHO technique uses a multi deep belief network (MDBN) model. The experimental results illustrated the promising performance of the ADPW-FLHHO technique over existing models with a maximum AUC score of 99.36%, 99.19%, and 98.90% on the University of California San Diego (UCSD) Ped1, UCSD Ped2, and avenue datasets, respectively. Therefore, the proposed model can be employed for accurate and automated anomaly detection in the ITS environment. Full article
(This article belongs to the Special Issue Remote Sensing for Intelligent Transportation Systems in Smart Cities)
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19 pages, 6778 KiB  
Article
Block-Scrambling-Based Encryption with Deep-Learning-Driven Remote Sensing Image Classification
by Faisal S. Alsubaei, Amani A. Alneil, Abdullah Mohamed and Anwer Mustafa Hilal
Remote Sens. 2023, 15(4), 1022; https://doi.org/10.3390/rs15041022 - 12 Feb 2023
Cited by 4 | Viewed by 1706
Abstract
Remote sensing is a long-distance measuring technology that obtains data about a phenomenon or an object. Remote sensing technology plays a crucial role in several domains, such as weather forecasts, resource surveys, disaster evaluation and environment protection. The application of remote-sensing images (RSIs) [...] Read more.
Remote sensing is a long-distance measuring technology that obtains data about a phenomenon or an object. Remote sensing technology plays a crucial role in several domains, such as weather forecasts, resource surveys, disaster evaluation and environment protection. The application of remote-sensing images (RSIs) is extensive in some specific domains, such as national security and business secrets. Simple multimedia distribution techniques and the development of the Internet make the content security of RSIs a significant problem for both engineers and scientists. In this background, RSI classification using deep learning (DL) models becomes essential. Therefore, the current research article develops a block-scrambling-based encryption with privacy preserving optimal deep-learning-driven classification (BSBE-PPODLC) technique for the classification of RSIs. The presented BSBE-PPODLC technique follows a two-stage process, i.e., image encryption and classification. Initially, the RSI encryption process takes place based on a BSBE approach. In the second stage, the image classification process is performed, and it encompasses multiple phases, such as densely connected network (DenseNet) feature extraction, extreme gradient boosting (XGBoost) classifier and artificial gorilla troops optimizer (AGTO)-based hyperparameter tuning. The proposed BSBE-PPODLC technique was simulated using the RSI dataset, and the outcomes were assessed under different aspects. The outcomes confirmed that the presented BSBE-PPODLC approach accomplished improved performance compared to the existing models. Full article
(This article belongs to the Special Issue Remote Sensing for Intelligent Transportation Systems in Smart Cities)
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19 pages, 5753 KiB  
Article
Road Damage Detection Using the Hunger Games Search with Elman Neural Network on High-Resolution Remote Sensing Images
by Mesfer Al Duhayyim, Areej A. Malibari, Abdullah Alharbi, Kallekh Afef, Ayman Yafoz, Raed Alsini, Omar Alghushairy and Heba Mohsen
Remote Sens. 2022, 14(24), 6222; https://doi.org/10.3390/rs14246222 - 8 Dec 2022
Cited by 6 | Viewed by 2262
Abstract
Roads can be significant traffic lifelines that can be damaged by collapsed tree branches, landslide rubble, and buildings debris. Thus, road damage detection and evaluation by utilizing High-Resolution Remote Sensing Images (RSI) are highly important to maintain routes in optimal conditions and execute [...] Read more.
Roads can be significant traffic lifelines that can be damaged by collapsed tree branches, landslide rubble, and buildings debris. Thus, road damage detection and evaluation by utilizing High-Resolution Remote Sensing Images (RSI) are highly important to maintain routes in optimal conditions and execute rescue operations. Detecting damaged road areas through high-resolution aerial images could promote faster and effectual disaster management and decision making. Several techniques for the prediction and detection of road damage caused by earthquakes are available. Recently, computer vision (CV) techniques have appeared as an optimal solution for road damage automated inspection. This article presents a new Road Damage Detection modality using the Hunger Games Search with Elman Neural Network (RDD–HGSENN) on High-Resolution RSIs. The presented RDD–HGSENN technique mainly aims to determine road damages using RSIs. In the presented RDD–HGSENN technique, the RetinaNet model was applied for damage detection on a road. In addition, the RDD–HGSENN technique can perform road damage classification using the ENN model. To tune the ENN parameters automatically, the HGS algorithm was exploited in this work. To examine the enhanced outcomes of the presented RDD–HGSENN technique, a comprehensive set of simulations were conducted. The experimental outcomes demonstrated the improved performance of the RDD–HGSENN technique with respect to recent approaches in relation to several measures. Full article
(This article belongs to the Special Issue Remote Sensing for Intelligent Transportation Systems in Smart Cities)
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