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Neuralisms Shenzhen
Neuralisms Shenzhen Trans-scalar Design and Artificial Intelligence
Introduction The ambition of the studio is to develop techniques and proposals that show how AI can be used to enable a more localized, sustainable, and adaptable planning process. By using a combination of satellite imagery analysis and AI generative methods, planners can move beyond individual proposals to networks of related proposals across the city.
Twenty-first-century cities like Shenzhen have grown into diverse, complex, and interconnected organisms. That interconnectedness brings an expanding range of constraints and demands —for new types of civic engagement, for new ways of integrating nature with the city, and for more sustainable use of resources. Instead of blunt “terrain vague” redevelopment strategies that can create enormous impacts and waste, we propose that development strategies be more calibrated to specific contexts and responsive to these interconnected demands.
Big data and smart city approaches are a start but are limited. These approaches have generated a torrent of information, but not always corresponding insight. Cities need ways to not only gather data but test scenarios in a more calibrated way.
Artificial Intelligence and data science approaches can create more adaptable, flexible, and localized solutions to planning and design problems. By using techniques from industrial surveillance — including methods of satellite image analysis — planners can get more precise and more localized insight into how to develop particular sites. AI can also be used generatively,
19 to create rule sets and organizations that can be easily applied to a range of sites more flexibly and easily than typical parametric approaches.
Introduction
Neuralisms Shenzhen 20