Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A System for Monitoring the Environment of Historic Places Using Convolutional Neural Network Methodologies

Version 1 : Received: 25 May 2021 / Approved: 26 May 2021 / Online: 26 May 2021 (13:06:34 CEST)

How to cite: Maria, M.D.; Fiumi, L.; Mazzei, M.; V., B.O. A System for Monitoring the Environment of Historic Places Using Convolutional Neural Network Methodologies. Preprints 2021, 2021050636 (doi: 10.20944/preprints202105.0636.v1). Maria, M.D.; Fiumi, L.; Mazzei, M.; V., B.O. A System for Monitoring the Environment of Historic Places Using Convolutional Neural Network Methodologies. Preprints 2021, 2021050636 (doi: 10.20944/preprints202105.0636.v1).

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.

Subject Areas

cultural heritage; environment; deep learning; artificial intelligence; neural network.

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