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The Role of Computer Vision in Facilitating Smart Urban Development: Opportunities and Impediments

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 April 2025 | Viewed by 1416

Special Issue Editors


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Guest Editor
1. Department of Computer Science, Cornell University, Ithaca, NY 14853, USA
2. Department of Hydraulics and Sanitation, Technology Sector, Federal University of Paraná, Curitiba 81531-990, Brazil
Interests: computer vision; deep learning; Internet of Things (IoT); AI; big data; pattern recognition
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
National Research Council of Italy, 10135 Rome, Italy
Interests: social computing; human-computer interaction; multimodal and natural language processing; user-centered interaction design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of computer vision technologies into urban environments marks the dawn of a groundbreaking era in smart city innovation. By harnessing the power of advanced imaging and analytics, cities around the world are being transformed into more efficient, sustainable, and livable communities. This Special Issue is dedicated to exploring the cutting-edge applications of computer vision within the smart city paradigm, seeking to illuminate pioneering research, inventive methodologies, and groundbreaking technologies that are propelling urban innovation forward.

We are particularly interested in contributions that delve deep into how computer vision is redefining urban management, enhancing environmental sustainability, and improving the overall quality of life for city dwellers. The scope of this Special Issue encompasses a wide array of applications—from traffic flow optimization and automated surveillance systems to pollution monitoring and infrastructure maintenance—all aimed at making cities smarter and more responsive to the needs of their inhabitants.

Contributions are sought that push the boundaries of current knowledge and practice, including but not limited to:

  • Advancements in computer vision algorithms for urban applications;Integration of computer vision in urban infrastructure management;
  • Computer vision for urban environmental protection and sustainability;
  • Enhancing urban safety and security with computer vision;
  • Smart transportation systems powered by computer vision;
  • Public space and crowd management solutions;
  • Impact of computer vision on urban planning and governance.

Prof. Dr. Heinz Dieter Fill
Dr. Arianna D'Ulizia
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. Applied Sciences 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 2400 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

  • computer vision
  • urban innovation
  • smart cities
  • environmental sustainability
  • urban safety and security
  • intelligent transportation systems
  • infrastructure management
  • public space management
  • urban planning
  • ethical implications

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Published Papers (1 paper)

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Research

20 pages, 2518 KiB  
Article
Designing and Implementing a Public Urban Transport Scheduling System Based on Artificial Intelligence for Smart Cities
by Cosmina-Mihaela Rosca, Adrian Stancu, Cosmin-Florinel Neculaiu and Ionuț-Adrian Gortoescu
Appl. Sci. 2024, 14(19), 8861; https://doi.org/10.3390/app14198861 - 2 Oct 2024
Viewed by 928
Abstract
Many countries encourage their populations to use public urban transport to decrease pollution and traffic congestion. However, this can generate overcrowded routes at certain times and low economic efficiency for public urban transport companies when buses carry few passengers. This article proposes a [...] Read more.
Many countries encourage their populations to use public urban transport to decrease pollution and traffic congestion. However, this can generate overcrowded routes at certain times and low economic efficiency for public urban transport companies when buses carry few passengers. This article proposes a Public Urban Transport Scheduling System (PUTSS) algorithm for allocating a public urban transport fleet based on the number of passengers waiting for a bus and considering the efficiency of public urban transport companies. The PUTSS algorithm integrates artificial intelligence (AI) methods to identify the number of people waiting at each station through real-time image acquisition. The technique presented is Azure Computer Vision. In a case study, the accuracy of correctly identifying the number of persons in an image was computed using the Microsoft Azure Computer Vision service. The proposed PUTSS algorithm also uses Google Maps Service for congestion-level identification. Employing these modern tools in the algorithm makes improving public urban transport services possible. The algorithm is integrated into a software application developed in C#, simulating a real-world scenario involving two public urban transport vehicles. The global accuracy rate of 89.81% demonstrates the practical applicability of the software product. Full article
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