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 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.

[1]  M McKee,et al.  Using remote sensing to define environmental characteristics related to physical activity and dietary behaviours: a systematic review (the SPOTLIGHT project). , 2014, Health & place.

[2]  Daniel Arribas-Bel,et al.  Accidental, open and everywhere: Emerging data sources for the understanding of cities , 2014 .

[3]  Julien O. Teitler,et al.  Using Google Street View to audit neighborhood environments. , 2011, American journal of preventive medicine.

[4]  Mauro Mazzei.  An Unsupervised Machine Learning Approach in Remote Sensing Data , 2019, ICCSA.

[5]  Bronwen T. Taylor,et al.  Measuring the quality of public open space using Google Earth. , 2011, American journal of preventive medicine.

[6]  A. Caspi,et al.  Systematic social observation of children's neighborhoods using Google Street View: a reliable and cost-effective method. , 2012, Journal of child psychology and psychiatry, and allied disciplines.

[7]  Mauro Mazzei,et al.  Comparative Analysis of Models of Location and Spatial Interaction , 2014, ICCSA.

[8]  Francesco Bandarin,et al.  The Historic Urban Landscape: Managing Heritage in an Urban Century , 2012 .

[9]  Jan Gehl,et al.  How To Study Public Life , 2013 .

[10]  L. Veldpaus,et al.  Urban Heritage: Putting the Past into the Future , 2013 .

[11]  Carlo Ratti,et al.  Mapping Urban Landscapes Along Streets Using Google Street View , 2017 .

[12]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[13]  Ron Van Oers.  TOWARDS NEW INTERNATIONAL GUIDELINES FOR THE CONSERVATION OF HISTORIC URBAN LANDSCAPES (HUL) , 2007 .

[14]  Mauro Mazzei,et al.  Spatial Multicriteria Analysis Approach for Evaluation of Mobility Demand in Urban Areas , 2017, ICCSA.

[15]  Matthew Carmona,et al.  Contemporary Public Space, Part Two: Classification , 2010 .

[16]  F. Bull,et al.  Development of a public open space desktop auditing tool (POSDAT): A remote sensing approach , 2013 .

[17]  Beniamino Murgante,et al.  Fuzzy definition of Rural Urban Interface: An application based on land use change scenarios in Portugal , 2018, Environ. Model. Softw..

[18]  Jieping Ye,et al.  Object Detection in 20 Years: A Survey , 2019, Proceedings of the IEEE.

[19]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[20]  Peng Gong,et al.  Can you see green? Assessing the visibility of urban forests in cities , 2009 .

[21]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Salvatore Di Guida,et al.  Spatial Data Warehouse and Spatial OLAP in Indoor/Outdoor Cultural Environments , 2018, ICCSA.

[23]  Lorenza Fiumi,et al.  Surveying the roofs of Rome , 2012 .

[24]  Mauro Mazzei,et al.  Spatial Statistical Models for the Evaluation of the Landscape , 2013, ICCSA.

[25]  Matthew Carmona,et al.  Contemporary Public Space: Critique and Classification, Part One: Critique , 2010 .

[26]  D. Nowak,et al.  Tree and forest effects on air quality and human health in the United States. , 2014, Environmental pollution.

[27]  Julia Jeyacheya,et al.  Evaluating the effectiveness of land-use zoning for the protection of built heritage in the Bagan Archaeological Zone, Myanmar—A satellite remote-sensing approach , 2019, Land Use Policy.

[28]  Malcolm Tait,et al.  A perceptual model of smellscape pleasantness , 2018, Cities.

[29]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

[30]  Li Yin,et al.  The impact of street network connectivity on pedestrian volume , 2015 .

[31]  Jian Kang,et al.  Acoustic comfort evaluation in urban open public spaces , 2005 .

[32]  A. Galderisi.  Parking Policies and Quality of Urban Environment Politiche della sosta e qualità dell'ambiente urbano , 2009 .

[33]  Chuanrong Zhang,et al.  Environmental inequities in terms of different types of urban greenery in Hartford, Connecticut , 2016 .

[34]  Andy P. Jones,et al.  Developing and testing a street audit tool using Google Street View to measure environmental supportiveness for physical activity , 2013, International Journal of Behavioral Nutrition and Physical Activity.

[35]  Mauro Mazzei.  An Unsupervised Machine Learning Approach for Medical Image Analysis , 2021 .

[36]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.