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A platform for research: civil engineering, architecture and urbanism
Mapping property redevelopment via GeoAI: Integrating computer vision and socioenvironmental patterns and processes
Abstract 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.
Highlights Geographical knowledge can improve the accuracy of deep convolutional neural networks. The negative impact of nonbuilding visual changes can be effectively reduced by our masked deep pyramid similarity model. Urban redevelopment should be understood as a locally tuned back-to-the-city process owing to contingencies at various scales.
Mapping property redevelopment via GeoAI: Integrating computer vision and socioenvironmental patterns and processes
Abstract 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.
Highlights Geographical knowledge can improve the accuracy of deep convolutional neural networks. The negative impact of nonbuilding visual changes can be effectively reduced by our masked deep pyramid similarity model. Urban redevelopment should be understood as a locally tuned back-to-the-city process owing to contingencies at various scales.
Mapping property redevelopment via GeoAI: Integrating computer vision and socioenvironmental patterns and processes
Liu, Cheng (author) / Song, Weixuan (author)
Cities ; 144
2023-10-20
Article (Journal)
Electronic Resource
English
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