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.
Abstract (translated)
城市环境中绿色空间的稀缺是一个关键挑战。这种短缺对公民的健康和福祉产生了多种不利影响。小规模干预措施，例如口袋公园，是一种可行的解决方案，但需要考虑多个限制，包括在特定区域的设计和实施。在这项研究中，我们利用生成式人工智能的多尺度干预规划功能，重点关注基于自然的解决方案。通过利用图像到图像和图像修复算法，我们提出了一种解决城市地区绿色空间不足的方法。聚焦于希腊塞萨洛尼基的两个小巷，其中绿化不足，我们证明了我们方法在可视化基于自然的干预措施方面的有效性。我们的研究结果强调了新兴技术在塑造城市干预规划过程未来方面的变革潜力。
URL
https://arxiv.org/abs/2404.15492
