Stephen Law
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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Index Terms
- An application of convolutional neural network in street image classification: the case study of london
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