Abstract
This research utilizes artificial intelligence (AI) by applying deep learning in architecture and urban design. The study's novelty is clarifying the applicability of deep learning to impressions of city landscapes as visual elements of design to differentiate "desire/no-desire to visit" and "degree of desire to visit". Street names were therefore classi ed as constituents of image consciousness. Thus, deduction AI that could achieve high precision from the viewpoint of psychological and medical treatment statistics is developed, and the con dence interval of 100 kinds of AI developed with this method is con rmed to be small. Arti cial intelligence, Deep learning, Inpressin, Sensibily, City landscape @@@@@@@@@@@@@@@@@@@@@ @@@@@@@@@@@@@@@@@@@@ @@@@@@@@@@@@@@@@@@@@@@@@@@@ @@@@@@@@@@@@@@@@@@@ @@@@@@@@@@@@@@@@@@@@@@@ This research utilizes artificial intelligence (AI) by applying deep learning in architecture and urban design. The study's novelty is clarifying the applicability of deep learning to impressions of city landscapes as visual elements of design to differentiate "desire/no-desire to visit" and "degree of desire to visit". Street names were therefore classi ed as constituents of image consciousness. Thus, deduction AI that could achieve high precision from the viewpoint of psychological and medical treatment statistics is developed, and the con dence interval of 100 kinds of AI developed with this method is con rmed to be small. Arti cial intelligence, Deep learning, Inpressin, Sensibily, City landscape @@@@@@@@@@@@@@@@@@@@@ @@@@@@@@@@@@@@@@@@@@ @@@@@@@@@@@@@@@@@@@@@@@@@@@ @@@@@@@@@@@@@@@@@@@ @@@@@@@@@@@@@@@@@@@@@@@ This research utilizes artificial intelligence (AI) by applying deep learning in architecture and urban design. The study's novelty is clarifying the applicability of deep learning to impressions of city landscapes as visual elements of design to differentiate "desire/no-desire to visit" and "degree of desire to visit". Street names were therefore classi ed as constituents of image consciousness. Thus, deduction AI that could achieve high precision from the viewpoint of
Cite
CITATION STYLE
YAMADA, S., & ONO, K. (2019). DEVELOPMENT AND VERIFICATION OF THE IMPRESSION DEDUCTION MODEL FOR CITY LANDSCAPE WITH DEEP LEARNING. Journal of Architecture and Planning (Transactions of AIJ), 84(759), 1323–1331. https://doi.org/10.3130/aija.84.1323
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