Urban Space Synthesis Based on Wave Function Collapse and Convolutional Neural Networks
Автор: Bo Lin
Загружено: 2020-05-15
Просмотров: 187
The research aims to develop an AI aided urban space synthesis approach for fast prototyping of urban design. The methods of wave function collapse algorithm and convolutional neural networks are utilized. Firstly, we establish an urban design database. Then, the street networks, urban block spatial forms and urban building function layouts are generated by WFC and CNNs and they are evaluated and selected by researcher afterwards. Finally, the 3D model is generated. We demonstrate the feasibility of our approach through the case study of the North Extension of Central Green Axis in Wenzhou. This approach improves the efficiency of urban design and provides new ways of thinking for architecture and urban design.
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