Multi-scale Intervention Planning based on Generative Design
This work addresses urban planning challenges for city residents by providing a method to visualize small-scale green interventions, though it is incremental in applying existing AI techniques to a specific domain.
The study tackled the problem of green space scarcity in urban environments by using generative AI for multi-scale intervention planning, demonstrating its efficacy in visualizing nature-based solutions for two alleys in Thessaloniki.
The scarcity of green spaces, in urban environments, consists a critical challenge. There are multiple adverse effects, impacting the health and well-being of the citizens. Small scale interventions, e.g. pocket parks, is a viable solution, but comes with multiple constraints, involving the design and implementation over a specific area. In this study, we harness the capabilities of generative AI for multi-scale intervention planning, focusing on nature based solutions. By leveraging image-to-image and image inpainting algorithms, we propose a methodology to address the green space deficit in urban areas. Focusing on two alleys in Thessaloniki, where greenery is lacking, we demonstrate the efficacy of our approach in visualizing NBS interventions. Our findings underscore the transformative potential of emerging technologies in shaping the future of urban intervention planning processes.