Special Issue "Artificial Intelligence for Environment and Cultural Heritage"

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

Deadline for manuscript submissions: 30 November 2021.

Special Issue Editors

Prof. Dr. Antonio M. Rinaldi
E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technology, University of Napoli Federico II, 81100 Napoli, Italy
Interests: artificial intelligence; big data; semantic information retrieval; knowledge representation; GIS
Prof. Dr. Beniamino Murgante
E-Mail Website
Guest Editor

Special Issue Information

Dear Colleagues,

Nowadays, Artificial Intelligence is a set of theories, methodologies and technologies which represents the key discipline for advanced application in different fields. Different cultural areas related to computer science, territorial planning and environmental research need to explore possible interactions based on artificial intelligence technologies. With this aim, AI represents the key factor for enabling the design of novel approaches, smart technologies and tools. The main purpose of this special issue is to publish original research on theoretical issues, innovative methods and applications based on artificial intelligence, big data and deep learning applied to different aspects of the knowledge management about environment and cultural heritage.

Prof. Dr. Antonio M. Rinaldi
Prof. Dr. Beniamino Murgante
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 papers will be 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 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

  • aritficial intelligence
  • digital cultural heritage
  • environmental safeguard and protection
  • big-data
  • deep learning
  • GIS
  • remote sensing

Published Papers (1 paper)

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Research

Article
Local Development and Gentrification Resulting from the Rehabilitation of Singular Buildings: Analysis of Neural Networks
Remote Sens. 2021, 13(8), 1500; https://doi.org/10.3390/rs13081500 - 13 Apr 2021
Viewed by 544
Abstract
The recovery of a built heritage and specifically of singular buildings is a key aspect of local development. The aim of this study was to understand the influence of these regenerations on their environment by transforming adjacent businesses and initiating parallel processes of [...] Read more.
The recovery of a built heritage and specifically of singular buildings is a key aspect of local development. The aim of this study was to understand the influence of these regenerations on their environment by transforming adjacent businesses and initiating parallel processes of gentrification and local development. The renewed attraction of these new businesses to the area can result in increased employment and production. The methodology used was based on self-organizing maps of neural networks with matrix architecture and competitive learning. Through the analysis of neural networks, we were able to identify common relationships and behaviors in commercial properties which are adjacent to singular buildings and that share common patterns and characteristics or attributes. The singular buildings analyzed are located along the Spanish Mediterranean coast in the cities of Almería, Barcelona, and Valencia. The results obtained were based on the following hypotheses: occupancy model and the classification based on total occupancy, total variation in occupancy, and the most common types of usage of a given ground floor commercial property. Among the conclusions, we highlight the existence of commercial premises that display anti-cyclical economic behavior and the presence of commercial premises considered to be “unfortunate” or with low potential. Full article
(This article belongs to the Special Issue Artificial Intelligence for Environment and Cultural Heritage)
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