Mapping property redevelopment via GeoAI: Integrating computer vision and socioenvironmental patterns and processes
文献类型：期刊论文
| 作者 | Liu, Cheng2; Song, Weixuan3 |
| 刊名 | CITIES
 |
| 出版日期 | 2024 |
| 卷号 | 144页码:104644 |
| 关键词 | Physical urban change Gentrification Regeneration Street view imagery Deep learning |
| DOI | 10.1016/j.cities.2023.104644 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Domain knowledge of social and environmental sciences is generally derived from less structured small data and/ or small models. The integration of deep learning with socioenvironmental and geographical patterns and processes is still in an early phase. This study proposes a flexible framework that synthesizes computer vision with patterns and processes of geographical phenomena (e.g., urban redevelopment) via deep convolutional neural networks and spatiotemporal neighborhoods, respectively. Meanwhile, undesirable visual cues (e.g., temporary objects such as cars) cause false alarms in urban change detection. Thus, a masked deep pyramid similarity model (i.e., the computer vision model) is proposed to minimize the negative impact of nonbuilding changes. This pipeline has robustness against obfuscation of undesirable street scene changes and elasticity of geographical knowledge representations in mapping property redevelopment. This model is reproducible and adaptable due to the wide availability of SVI and flexibility of the framework. The results suggest that the domain-driven rules and nonbuilding change masking mechanism can significantly increase the accuracy of a computer vision model. We also find that urban redevelopment should be understood as a locally tuned back-to the-city process with weak replicability. Hybrid gentrification (i.e., the combination of seesaw gentrification and continuous gentrification) is observed globally with local variations. Takeaway for practice: First, the population of those affected should be carefully identified and the benefits of gentrification need to be redistributed on a larger scale. For Auckland, decision-makers should not divert attention away from low-income tenants in outer suburbs that slipped below the radar of local government and scholars. Second, more attention should be given to less resourcing neighborhoods in constantly gentrifying neighborhoods when governments formulate and implement housing subsidy policies. Locally, incessant redevelopment in Auckland CBD, New Market and Remuera, etc., should be contained. |
| WOS关键词 | GOOGLE STREET VIEW ; LAND-COVER ; NEURAL-NETWORKS ; URBAN CHANGES ; TIME-SERIES ; NEW-YORK ; BIG DATA ; GENTRIFICATION ; IMAGERY ; CLASSIFICATION |
| WOS研究方向 | Urban Studies |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:001112606400001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/201007]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Chinese Acad Sci, Nanjing Inst Geog & Limnol, Nanjing 210008, Peoples R China 2.China Univ Geosci, Sch Publ Adm, 388 Lumo Rd, Wuhan 430074, Peoples R China 3.State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Cheng,Song, Weixuan. Mapping property redevelopment via GeoAI: Integrating computer vision and socioenvironmental patterns and processes[J]. CITIES,2024,144:104644. |
| APA | Liu, Cheng,&Song, Weixuan.(2024).Mapping property redevelopment via GeoAI: Integrating computer vision and socioenvironmental patterns and processes.CITIES,144,104644. |
| MLA | Liu, Cheng,et al."Mapping property redevelopment via GeoAI: Integrating computer vision and socioenvironmental patterns and processes".CITIES 144(2024):104644. |
入库方式： OAI收割
来源：地理科学与资源研究所
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