Maria, M.D.; Fiumi, L.; Mazzei, M.; V., B.O. A System for Monitoring the Environment of Historic Places Using Convolutional Neural Network Methodologies. Heritage2021, 4, 1429-1446.
Maria, M.D.; Fiumi, L.; Mazzei, M.; V., B.O. A System for Monitoring the Environment of Historic Places Using Convolutional Neural Network Methodologies. Heritage 2021, 4, 1429-1446.
Maria, M.D.; Fiumi, L.; Mazzei, M.; V., B.O. A System for Monitoring the Environment of Historic Places Using Convolutional Neural Network Methodologies. Heritage2021, 4, 1429-1446.
Maria, M.D.; Fiumi, L.; Mazzei, M.; V., B.O. A System for Monitoring the Environment of Historic Places Using Convolutional Neural Network Methodologies. Heritage 2021, 4, 1429-1446.
Abstract
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 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.
Keywords
cultural heritage; environment; deep learning; artificial intelligence; neural network.
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
