He Wanyu, Li Chun, Nie Guangyang, Jackie Yong Leong Shong, Wang Chuyu

Keywords： Artificial Intelligence; Deep Learning; Convolutional Neural Network; Image Discrimination; Urban Perception

Abstract：

Nowadays, machine learning attracts intense attention from artificial intelligence researches and extends a variety of applications such as image discrimination, voice assistant and smart translator. In particular, image discrimination has been extensively studied and practiced in various industries, including urban field. Thanks to Convolutional Neural Network (CNN) based on Deep Learning (DL) that has made remarkable achievements in computer vision, it is more efficient to train computer to discriminate architecture styles, urban texture and other urban features. Based on image discrimination by DL, this research focuses on exploring the applications of CNN in the field of urban perception. In consideration of limits and errors brought by training customized image discrimination model with the existing open source labeled image dataset, this paper explores a whole process from collecting data, self-constructing training dataset to building a customized image discrimination model which satisfies specific requirements. The latent application of DL in urban scale are discussed through three experiment cases: the cityscape analysis, urban problem detection and urban pattern evaluation.

Funds：

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