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Smart Sensors and Artificial Intelligence for Sustainable Urban and Territorial Planning, Management and Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 2201

Special Issue Editors


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Guest Editor
Department of Civil Engineering, University of Calabria, Rende, Italy
Interests: intelligent transportation systems (ITS); microsimulation; artificial intelligence; road safety; public transport
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Calabria, 87036 Rende, Italy
Interests: methods and models for urban and territorial planning; management and control; remote sensing techniques for the monitoring and control of territorial transformations processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

In the last few years, rapid population growth has led to the unplanned urbanization and uncontrolled built-up development determining negative impacts, such as soil consumption, farmland loss, the depletion of natural resources, loss of ecosystem services and pressure on urban resilience, which represent a serious threat to assets and lives.  

Recent technological advances in sensors and artificial intelligence (AI) technologies have led to the development of sustainable urban and territorial planning, management and monitoring processes, encouraging the efficient use of land and the decodification of natural and anthropic hazards.  

Multi-temporal RS data, including optical RS data, synthetic aperture radar (SAR) data, street view images, and combined heterogeneous data, can, for example, provide abundant information to identify land use and land cover (LULC) changes in a specific area across a period of time and a rapid mapping of degraded areas, playing a fundamental role in more effectively and efficiently reducing the disruptive environmental, social and economic impacts of critical situations. 

This Special Issue encourages original and high-quality studies on the use of innovative technologies for urban and territorial planning, management and monitoring, focusing on, but not limited to, sustainable land development through the analysis of LULC and the implementation of innovative natural and anthropic hazards mitigation measures. In addition, this Special Issue will accept review manuscripts that show the state-of-the-art and potential of both advanced systems and applications on the topic of intelligent solutions for sustainable urban and territorial planning, management and monitoring. 

Potential topics include but are not limited to the following: 

  • Mobile apps and AI-integrated applications for sustainable urban and territorial planning, management and monitoring. 
  • Remote sensing data-based methodologies for sustainable urban and territorial planning, management and monitoring. 
  • Simulation, deployment, and tested software platforms for sustainable urban and territorial planning, management and monitoring. 
  • Accessibility, resilience, and security of IoT infrastructures for sustainable urban and territorial planning, management and monitoring. 
  • AI techniques for devices and embedded systems for sustainable urban and territorial planning, management and monitoring. 
  • Review manuscripts on intelligent advanced systems and their applications for sustainable urban and territorial planning, management and monitoring. 

Dr. Alessandro Vitale
Prof. Mauro Francini
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. Sensors 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 2600 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

  • sustainable urban and territorial planning, management and monitoring
  • spatial planning
  • efficient land use
  • land use and land cover mapping and changes
  • natural hazards and anthropic disasters
  • big data analytics
  • artificial intelligence models
  • deep learning and machine learning methods
  • smart sensors
  • multi-source remote sensing data
  • simulations and tested software platforms for remote sensing data processing

Published Papers (1 paper)

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Research

21 pages, 19314 KiB  
Article
Combining Deep Learning and Multi-Source GIS Methods to Analyze Urban and Greening Changes
by Mauro Francini, Carolina Salvo and Alessandro Vitale
Sensors 2023, 23(8), 3805; https://doi.org/10.3390/s23083805 - 07 Apr 2023
Cited by 5 | Viewed by 1840
Abstract
Although many authors have observed a degradation in greening cover alongside an increase in the built-up areas, resulting in a deterioration of the essential environmental services for the well-being of ecosystems and society, few studies have measured how greening developed in its full [...] Read more.
Although many authors have observed a degradation in greening cover alongside an increase in the built-up areas, resulting in a deterioration of the essential environmental services for the well-being of ecosystems and society, few studies have measured how greening developed in its full spatiotemporal configuration with urban development using innovative remote sensing (RS) technologies. Focusing on this issue, the authors propose an innovative methodology for the analysis of the urban and greening changes over time by integrating deep learning (DL) technologies to classify and segment the built-up area and the vegetation cover from satellite and aerial images and geographic information system (GIS) techniques. The core of the methodology is a trained and validated U-Net model, which was tested on an urban area in the municipality of Matera (Italy), analyzing the urban and greening changes from 2000 to 2020. The results demonstrate a very good level of accuracy of the U-Net model, a remarkable increment in the built-up area density (8.28%) and a decline in the vegetation cover density (5.13%). The obtained results demonstrate how the proposed method can be used to rapidly and accurately identify useful information about urban and greening spatiotemporal development using innovative RS technologies supporting sustainable development processes. Full article
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