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An application of convolutional neural network in street image classification: the case study of london

Published: 07 November 2017 Publication History

Abstract

Street frontage quality is an important element in urban design as it contributes to the interest, social life and success of public spaces. To collect the data needed to evaluate street frontage quality at the city or regional level using traditional survey method is both costly and time consuming. As a result, this research proposes a pipeline that uses convolutional neural network to classify the frontage of a street image through the case study of Greater London. A novelty of the research is it uses both Google streetview images and 3D-model generated streetview images for the classification. The benefit of this approach is that it can provide a framework to test different urban parameters to help evaluate future urban design projects. The research finds encouraging results in classifying urban frontage quality using deep learning models. This research also finds that augmenting the baseline model with images produced from a 3D-model can improve slightly the accuracy of the results. However these results should be taken as preliminary, where we acknowledge several limitations such as the lack of adversarial analysis, labeled data, or parameter tuning. Despite these limitations, the results of the proof-of-concept study is positive and carries great potential in the application of urban data analytics.

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  • (2022)Automatic Classification of Photos by Tourist Attractions Using Deep Learning Model and Image Feature Vector ClusteringISPRS International Journal of Geo-Information10.3390/ijgi1104024511:4(245)Online publication date: 10-Apr-2022
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Published In

cover image ACM Other conferences
GeoAI '17: Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery
November 2017
57 pages
ISBN:9781450354981
DOI:10.1145/3149808
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 November 2017

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Author Tags

  1. London
  2. convolutional neural network
  3. deep learning
  4. machine vision
  5. urban design

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  • Research-article

Funding Sources

  • The Alan Turing Institute under the UK Engineering and Physical Sciences Research Council (EPSRC)

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SIGSPATIAL'17

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Overall Acceptance Rate 17 of 25 submissions, 68%

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Cited By

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  • (2024)Geospatial AI Concepts and FundamentalsRecent Trends in Geospatial AI10.4018/979-8-3693-8054-3.ch001(1-26)Online publication date: 13-Dec-2024
  • (2024)Redefining Age-Friendly Neighbourhoods: Translating the Promises of Blue Zones for Contemporary Urban EnvironmentsInternational Journal of Environmental Research and Public Health10.3390/ijerph2103036521:3(365)Online publication date: 19-Mar-2024
  • (2022)Automatic Classification of Photos by Tourist Attractions Using Deep Learning Model and Image Feature Vector ClusteringISPRS International Journal of Geo-Information10.3390/ijgi1104024511:4(245)Online publication date: 10-Apr-2022
  • (2022)HUMAN BEHAVIOR PREDICTION FOR CITYSCAPE IMAGES USING MULTIMODAL DEEP LEARNINGマルチモーダル深層学習を用いた街並み画像に対する人間の振る舞い予測Journal of Architecture and Planning (Transactions of AIJ)10.3130/aija.87.160287:798(1602-1611)Online publication date: 1-Aug-2022
  • (2022)GeoAI at ACM SIGSPATIALSIGSPATIAL Special10.1145/3578484.357849113:3(21-32)Online publication date: 23-Dec-2022
  • (2022)Classifying Tourists’ Photos and Exploring Tourism Destination Image Using a Deep Learning ModelJournal of Quality Assurance in Hospitality & Tourism10.1080/1528008X.2021.199556723:6(1480-1508)Online publication date: 10-Feb-2022
  • (2021)Transfer Learning of a Deep Learning Model for Exploring Tourists’ Urban Image Using Geotagged PhotosISPRS International Journal of Geo-Information10.3390/ijgi1003013710:3(137)Online publication date: 4-Mar-2021
  • (2021)Using Convolutional Neural Networks to Map Houses Suitable for Electric Vehicle Home ChargingAI10.3390/ai20100092:1(135-149)Online publication date: 16-Mar-2021
  • (2021)Making Green Transport a Reality: A Classification Based Data Analysis Method to Identify Properties Suitable for Electric Vehicle Charging Point Installation2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS10.1109/IGARSS47720.2021.9553748(6229-6232)Online publication date: 11-Jul-2021
  • (2020)HierarchyNet: Hierarchical CNN-Based Urban Building ClassificationRemote Sensing10.3390/rs1222379412:22(3794)Online publication date: 19-Nov-2020
  • Show More Cited By

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