heritage Article A System for Monitoring the Environment of Historic Places Using Convolutional Neural Network Methodologies Massimo De Maria 1 , Lorenza Fiumi 2 , Mauro Mazzei 3, * and Bik Oleg V. 1   Citation: 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. https:// doi.org/10.3390/heritage4030079 Academic Editor: Alessandro Sebastiani Received: 25 May 2021 Accepted: 20 July 2021 Published: 28 July 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional afﬁl- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Peoples Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, 117198 Moscow, Russia; de-mm@rudn.ru (M.D.M.); bik-ov@rudn.ru (B.O.V.) 2 National Research Council, Istituto di Ingegneria del Mare (INM), 139 Rome, Italy; lorenza.ﬁumi@cnr.it 3 National Research Council, Istituto di Analisi dei Sistemi ed Informatica, LabGeoInf, Via dei Taurini, 19, I-00185 Rome, Italy * Correspondence: mauro.mazzei@iasi.cnr.it 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 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. Keywords: cultural heritage; environment; deep learning; artiﬁcial intelligence; neural network 1. Introduction Google Maps and Street View were not developed for scientiﬁc research; however they create interesting research possibilities in the urban environment. Google Street View (GSV) was released in 2007 and differs from traditional mapping software by directly capturing the visual aspect at ground level. By blending together images taken from different angles, Street View creates what appears to be a seamless tour of the city streets and can give the feeling of “being there” [1]. GSV’s images have been studied in the computer vision community, although they were not created for research. At different times and modalities, these studies included the auditing of public open spaces [2,3], or neighborhood environmental audits [4–7], or recognizing urban identi- ties [8], or acoustic comfort [9], or the comfort of sensations [10], or aspects of sociability, or attachment to place [11]. There are other sources of data that can be used to understand, study and measure the urban environment. For example, remote sensing with high spatial resolution has been used for the study of street greenery in historic centers [12], for the estimation of the height of buildings and for the extraction of features in the historical urban landscape and for the classiﬁcation of roofs in the historic center of Rome [13]. However, remote data with high spatial resolution are not always available. Further- more, the proﬁle view of road landscapes, which people experience and see from eye level, is different from the top view captured in remotely sensed images [1,14]. These differences can be overcome with manual inventories and ﬁeld surveys; however, the collection of data in situ is laborious, time-consuming and allows for detection errors, especially if carried Heritage 2021, 4, 1429–1446. https://doi.org/10.3390/heritage4030079 https://www.mdpi.com/journal/heritage