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.

翻译：城市环境中绿色空间的匮乏构成了一项严峻挑战，对市民的身心健康产生多种不利影响。小规模干预措施（如口袋公园）虽为可行方案，但在特定区域的设计与实施过程中面临诸多约束。本研究借助生成式人工智能的潜力，聚焦基于自然的解决方案，开展多尺度干预规划。通过利用图像到图像转换与图像修复算法，我们提出了一种应对城市地区绿色空间短缺的方法论。以塞萨洛尼基两处缺乏绿化的巷道为例，我们验证了该方法在可视化基于自然的解决方案干预效果方面的有效性。研究结果凸显了新兴技术在塑造未来城市干预规划流程中的变革潜力。