Research on Sustainable Reuse of Urban Ruins Based on Artificial Intelligence Technology: A Study of Guangzhou

Qi Duan, Lihui Qi (), Renyu Cao and Peng Si
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Qi Duan: Guangzhou Panyu Polytechnic, Guangzhou 510000, China
Lihui Qi: Guangzhou Panyu Polytechnic, Guangzhou 510000, China
Renyu Cao: School of Fine Arts, Liaoning Normal University, Dalian 116000, China
Peng Si: State Key Laboratory of Explosive Science and Technology, Beijing Institute of Technology, Beijing 100081, China

Sustainability, 2022, vol. 14, issue 22, 1-28

Abstract: In recent years, with the continuous deepening of the urbanization process, the problem of urban ruins (URs) has become prominent. This significantly affects the happiness of residents around the URs, the overall image of the city, and the environment, and it has become an important issue in urban construction. At present, the types of urban ruins mainly include industrial ruins, abandoned urban buildings, and war sites. Generally, methods such as demolition and reconstruction of original buildings or upgrading and transformation are used to reuse URs, and some of them have achieved fruitful results. However, the current renovation of URs is based on fragmented renovation strategies for different URs without a systematic and universally applicable renovation methodology. With the development of artificial intelligence, technologies such as Generative Adversarial Network (GAN), Easy DL, and Natural Language Processing (NLP) can provide technical support for urban ruin reconstruction, from design to operation. Specifically in the present study, the ten representative URs in Guangzhou are first evaluated by the Analytic Hierarchy Process and then combined with AI methods, such as the adversarial generative networks and big data applications, into the reuse design of URs. Finally, a complete research system is established to implement URs’ projects, which provides a clearer systematic planning strategy for the reuse of URs in the future.

Keywords: artificial intelligence technology; urban ruins reuse; urban development; deep learning; generative adversarial network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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