著者

山田 悟史 大野 耕太郎

出版者

日本建築学会

雑誌

日本建築学会計画系論文集 (ISSN:13404210)

巻号頁・発行日

vol.84, no.759, pp.1323-1331, 2019 (Released:2019-05-30)

参考文献数

23

被引用文献数

7 7

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This research is a basic study on utilizing artificial intelligence (AI) by applying deep learning to the fields of architecture and urban design.  In recent years, the use of budding technologies, such as deep learning, has increased in the field of architecture and urban design. While this technology has potential in various fields, this study focuses on learning and deduction of sensibility evaluation and impression of design. Needless to say, the relationship of design with sensibility and impression is important, and the design heightens sensibility and impressions. However, the causal relationship of quantitative representation (feature value) and feature value of design with impression is complex and is characteristically difficult to deduce. Such a characteristic is a property similar to fields where deep learning has been successfully used. It is, therefore, thought that AI using deep learning could be applicable. As mentioned, this research examines the budding properties of AI that deduce “street names and desire to visit” based on city landscapes. Specifically, the “desire/no-desire to visit (classification)” and “degree of desire to visit” are deduced, and as constituents of image consciousness, street names are also classified (21 classes).  The object of the study and the city landscapes were prepared from 21 streets selected from a large city and sightseeing information. The images for city landscapes were obtained from street view on Google Earth to ensure that these images were not of any one building, ground, or sky. A total of 2,100 images, 100 for each street, were considered.  Deduction AI with high precision was first successfully developed to deduce “classification of street names (21 classes)”. Its precision was approximately 86% for the F-value with a K-coefficient of 0.8508 (p-value = 1.6e-15). Next, for the classification, deduction AI with high conformity with desire/no-desire to visit criteria of test subjects was successfully prepared. Its precision had a K-coefficient of 0.8920 (p-value = 2.2e-15). Further, for deducing degree of desire, there was little difference in the degree of desire to visit between test subjects, and AI permitting deduction with high correlation was successfully developed. For its precision, the effect size of Wilcoxon’s signed rank test (test of paired nonparametric data) was 0.18, and Spearman’s rank correlation was 0.7564 (p-value = 0.0005742). Finally, to generalize the methodology of AI using deep learning, the 95% confidence interval that considered 100 kinds of AI developed using this method was confirmed to be small. Specifically, the effect size did not exceed 0.2 (a threshold value indicating small effect size) and did not fall below 0.6 (a threshold value indicating high correlation). Under the experimental conditions of this study, the AI developed using deep learning can be described as a method that presents generality in the degree of precision.  From the perspective of the experimental conditions of the study and usage, a successful impression deduction AI for city landscapes with good precision is developed. This provides the first step in systematically organizing and investigating the hitherto unstudied budding potential of deep learning in the fields of architecture and urban design.

著者

山田 悟史 大野 耕太郎

出版者

日本建築学会

雑誌

日本建築学会計画系論文集 (ISSN:13404210)

巻号頁・発行日

vol.85, no.770, pp.987-995, 2020 (Released:2020-04-30)

参考文献数

27

被引用文献数

1

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In recent years, there has been a growing interest in the application of Deep Learning to architecture and urbanism. This research is focused on content generation AI using Deep Learning. Despite claims that replacing creativity-related work with machines is difficult, the use of generative adversarial networks (GANs) is becoming more popular in various fields. The objective of this research is to develop an AI-supported design or a co-creation between humans and AI through the application of GANs. The primary goal of this work could be interpreted as repurposing existing concepts to create new designs through the combination of multiple design sources. Therefore, the purpose of this research is the creation of AI that emulate and support the design process.  This research examines two types of AI through a two-stage process; the first is an AI that reproduces design, and the second is an AI that generates design. The first type of AI reproduces designs from different sources and includes an analysis of whether the design can be expressed mathematically. This analysis is a prerequisite for the creation of the second type of AI that generates new designs by combining information from multiple sources. In other words, the second type of AI views designs mathematically, and the possibility of expressing designs mathematically (using the first type of AI) is examined to ensure that such a function is feasible and in line with user intention. Here, a mathematical expression refers to a 100-dimensional vector and an already-learned deep neural network.  The AI that reproduces design was applied to famous cityscapes (Kyoto and Edinburgh) and the façades of famous buildings (three works by Le Corbusier). The designs were reproduced as images and used for subject experiments to confirm that the intended impressions (oriental and occidental) and the designs of each type were successfully reproduced.  For the AI that generates design, a new design was generated from calculations of different combinations (three pairs and one trio) of the façades of three works by Le Corbusier (church of Saint-Pierre, Notre Dame du Haut, and Villa Savoye). This design was subsequently used for text mining Bayesian-estimation-based subject experiments to confirm that the characteristics of the design sources were successfully inherited.  To the best of our knowledge, these are new types of AI. Further, we believe that these achievements may facilitate better dissemination of design through fast generation of a large number of images (design patterns) that constitute new types of designs. This achievement may also help expand the concept of human design thinking by suggesting designs that can be permuted using AI but otherwise inconceivable for human designers. Ultimately, this can help in the creation of a new design environment, namely “co-creation between humans and AI,” wherein the designers choose the sources and the AI generates a number of design choices for the final design.

著者

大野 耕太郎 山田 悟史 宗本 晋作

出版者

日本建築学会

雑誌

日本建築学会計画系論文集 (ISSN:13404210)

巻号頁・発行日

vol.87, no.798, pp.1602-1611, 2022-08-01 (Released:2022-08-01)

参考文献数

17

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This study aimed to estimate human willingness to visit cityscape images via artificial intelligence (AI) using multimodal deep learning. In this study, gaze information was acquired through subject experiments using a measurement device. We added gaze information when humans felt motivated to visit the cityscape image, and confirmed whether the estimation accuracy of AI would improve. We also created an AI model that generated gaze-view images, and used it for multimodal deep learning. We used pix2pix to generate the images. Finally, we verified the accuracy of the proposed multimodal deep learning approach, when the generated pseudo-gaze image was attached.