| id |
caadria2023_282 |
| authors |
Qin, Bowen and Zheng, Hao |
| year |
2023 |
| title |
An Image-Based Machine Learning Method for Urban Features Prediction With Three-Dimensional Building Information |
| doi |
https://doi.org/10.52842/conf.caadria.2023.1.109
|
| source |
Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 109–118 |
| summary |
Machine learning has been proven to be a very efficient tool in urban analysis, using models trained with big data. We have seen research that applies a generative adversarial network (GAN) to train models, feeding the street map and visualized urban characteristics to predict certain urban features. However, in most cases, the input map is a two-dimensional (2D) map that only stores the land type data (e.g., building, street, green space), hence reducing building information to only the ground-floor area. The identities of buildings with similar floor areas can be hugely different, which may contribute to the prediction errors in previous machine-learning models. In this research, we emphasize the importance of the use of an image-based neural network to analyze the relationship between urban features and the constructed environment. We compare the model that uses traditional street color maps as the input set, against a new input set with more detailed building data. Once trained, the model with the enhanced input set yields output at a higher level of accuracy in certain areas. We apply the new model framework to three selected urban features predictions: rental price, building energy cost, and food sanitary ratio. A broad range of new research could be conduct with our new framework. |
| keywords |
Artificial Intelligence, Generative adversarial network, Urban features, Building elevation, Open-source data, Prediction |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (2,278,262 bytes) |
| references |
Content-type: text/plain
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| last changed |
2023/06/15 23:14 |
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