A System for Monitoring the Environment of Historic Places Using Convolutional Neural Network Methodologies
This work aims to contribute to better understanding the use of public street spaces. (1) Background: In this sense, with a multidisciplinary approach, the objective of this work is to propose an experimental and reproducible method on a large scale. (2) Study area: The applied methodology uses arti...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-07-01
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| Series: | Heritage |
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| Online Access: | https://www.mdpi.com/2571-9408/4/3/79 |
| _version_ | 1797519035891449856 |
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| author | Massimo De Maria Lorenza Fiumi Mauro Mazzei Bik Oleg V. |
| author_facet | Massimo De Maria Lorenza Fiumi Mauro Mazzei Bik Oleg V. |
| author_sort | Massimo De Maria |
| collection | DOAJ |
| description | This work aims to contribute to better understanding the use of public street spaces. (1) Background: In this sense, with a multidisciplinary approach, the objective of this work is to propose an experimental and reproducible method on a large scale. (2) Study area: The applied methodology uses artificial intelligence to analyze Google Street View (GSV) images at street level. (3) Method: The purpose is to validate a methodology that allows us to characterize and quantify the use (pedestrians and cars) of some squares in Rome belonging to different historical periods. (4) Results: Through the use of machine vision techniques, typical of artificial intelligence and which use convolutional neural networks, a historical reading of some selected squares is proposed, with the aim of interpreting the dynamics of use and identifying some critical issues in progress. (5) Conclusions: This work validated the usefulness of a method applied to the use of artificial intelligence for the analysis of GSV images at street level. |
| first_indexed | 2024-03-10T07:37:34Z |
| format | Article |
| id | doaj.art-426f14c299fc46b8976abc72923c39da |
| institution | Directory Open Access Journal |
| issn | 2571-9408 |
| language | English |
| last_indexed | 2024-03-10T07:37:34Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Heritage |
| spelling | doaj.art-426f14c299fc46b8976abc72923c39da2023-11-22T13:19:44ZengMDPI AGHeritage2571-94082021-07-01431429144610.3390/heritage4030079A System for Monitoring the Environment of Historic Places Using Convolutional Neural Network MethodologiesMassimo De Maria0Lorenza Fiumi1Mauro Mazzei2Bik Oleg V.3Peoples Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, 117198 Moscow, RussiaNational Research Council, Istituto di Ingegneria del Mare (INM), 139 Rome, ItalyNational Research Council, Istituto di Analisi dei Sistemi ed Informatica, LabGeoInf, Via dei Taurini, 19, I-00185 Rome, ItalyPeoples Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, 117198 Moscow, RussiaThis work aims to contribute to better understanding the use of public street spaces. (1) Background: In this sense, with a multidisciplinary approach, the objective of this work is to propose an experimental and reproducible method on a large scale. (2) Study area: The applied methodology uses artificial intelligence to analyze Google Street View (GSV) images at street level. (3) Method: The purpose is to validate a methodology that allows us to characterize and quantify the use (pedestrians and cars) of some squares in Rome belonging to different historical periods. (4) Results: Through the use of machine vision techniques, typical of artificial intelligence and which use convolutional neural networks, a historical reading of some selected squares is proposed, with the aim of interpreting the dynamics of use and identifying some critical issues in progress. (5) Conclusions: This work validated the usefulness of a method applied to the use of artificial intelligence for the analysis of GSV images at street level.https://www.mdpi.com/2571-9408/4/3/79cultural heritageenvironmentdeep learningartificial intelligenceneural network |
| spellingShingle | Massimo De Maria Lorenza Fiumi Mauro Mazzei Bik Oleg V. A System for Monitoring the Environment of Historic Places Using Convolutional Neural Network Methodologies Heritage cultural heritage environment deep learning artificial intelligence neural network |
| title | A System for Monitoring the Environment of Historic Places Using Convolutional Neural Network Methodologies |
| title_full | A System for Monitoring the Environment of Historic Places Using Convolutional Neural Network Methodologies |
| title_fullStr | A System for Monitoring the Environment of Historic Places Using Convolutional Neural Network Methodologies |
| title_full_unstemmed | A System for Monitoring the Environment of Historic Places Using Convolutional Neural Network Methodologies |
| title_short | A System for Monitoring the Environment of Historic Places Using Convolutional Neural Network Methodologies |
| title_sort | system for monitoring the environment of historic places using convolutional neural network methodologies |
| topic | cultural heritage environment deep learning artificial intelligence neural network |
| url | https://www.mdpi.com/2571-9408/4/3/79 |
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