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Deep mapping gentrification in a large Canadian city using deep learning and Google Street View / Lazar Ilic in Plos one, vol 14 n° 3 (March 2019)  

Public [article]  inPlos one > vol 14 n° 3 (March 2019) . - n° e0212814 Titre : Deep mapping gentrification in a large Canadian city using deep learning and Google Street View Type de document : Article/Communication Auteurs : Lazar Ilic, Auteur ; M. Sawada, Auteur ; Amaury Zarzelli, Auteur Année de publication : 2019 Projets : 3-projet - voir note / Article en page(s) : n° e0212814 Note générale : bibliographie This work was supported by and is a contribution to the Ottawa Neighbourhood Study (www.neighbourhoodstudy.ca). Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] analyse socio-économique [Termes IGN] apprentissage profond [Termes IGN] Canada [Termes IGN] image Streetview [Termes IGN] quartier [Termes IGN] ville Résumé : (auteur) Gentrification is multidimensional and complex, but there is general agreement that visible changes to neighbourhoods are a clear manifestation of the process. Recent advances in computer vision and deep learning provide a unique opportunity to support automated mapping or ‘deep mapping’ of perceptual environmental attributes. We present a Siamese convolutional neural network (SCNN) that automatically detects gentrification-like visual changes in temporal sequences of Google Street View (GSV) images. Our SCNN achieves 95.6% test accuracy and is subsequently applied to GSV sequences at 86110 individual properties over a 9-year period in Ottawa, Canada. We use Kernel Density Estimation (KDE) to produce maps that illustrate where the spatial concentration of visual property improvements was highest within the study area at different times from 2007–2016. We find strong concordance between the mapped SCNN results and the spatial distribution of building permits in the City of Ottawa from 2011 to 2016. Our mapped results confirm those urban areas that are known to be undergoing gentrification as well as revealing areas undergoing gentrification that were previously unknown. Our approach differs from previous works because we examine the atomic unit of gentrification, namely, the individual property, for visual property improvements over time and we rely on KDE to describe regions of high spatial intensity that are indicative of gentrification processes. Numéro de notice : A2019-165 Affiliation des auteurs : ENSG+Ext (2012-2019) Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1371/journal.pone.0212814 Date de publication en ligne : 13/03/2019 En ligne : https://doi.org/10.1371/journal.pone.0212814 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99693 [article]