Affiliation:
1. Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Republic of Korea
2. Smart City Research Center, Advanced Institute of Convergence Technology, Seoul National University, Suwon 16229, Republic of Korea
Abstract
Artificial intelligence (AI) has demonstrated its ability to complete complex tasks in various fields. In urban studies, AI technology has been utilized in some limited domains, such as control of traffic and air quality. This study uses AI to better understand diverse urban studies data through a novel approach that uses a convolutional neural network (CNN). In this study, a building outline in the form of a two-dimensional image is used with its corresponding metadata to test the applicability of CNN in reading urban data. MobileNet, a high-efficiency CNN model, is trained to predict the location of restaurants in each building in Seoul, Korea. Consequently, using only 2D image data, the model satisfactorily predicts the locations of restaurants (AUC = 0.732); the model with 2D images and their metadata has higher performance but has an overfitting problem. In addition, the model using only 2D image data accurately predicts the regional distribution of restaurants and shows some typical urban forms with restaurants. The proposed model has several technical limitations but shows the potential to provide a further understanding of urban settings.
Funder
Ministry of Land, Infrastructure and Transport
Ministry of Education and the National Research Foundation of the Republic of Korea
Integrated Research Institute of Construction and Environmental Engineering and Institute of Engineering Research at Seoul National University
Subject
Earth and Planetary Sciences (miscellaneous),Computers in Earth Sciences,Geography, Planning and Development