Multi-scale Intervention Planning based on Generative Design

TL;DR

This paper presents a generative AI-based methodology for multi-scale urban green space interventions, demonstrating its effectiveness in visualizing nature-based solutions to address green space deficits in cities.

Contribution

It introduces a novel approach using image-to-image and inpainting algorithms for planning urban green interventions at multiple scales.

Findings

Effective visualization of green space interventions in Thessaloniki

Potential to transform urban planning with AI technologies

Addresses green space deficits through innovative generative methods

Abstract

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.