Generative AI text-to-image for community participation in landscape planning

• Generative AI text-to-image tools are increasingly used for community engagement. • We propose a new approach of integrating generative AI text-to-image tools in the process of participatory design. • We compromise the pace of translating participatory discussions into images, balancing between real-time approaches and extended periods. • This approach involves using ‘controlled imperfect’ generative images. • We collect keywords during the participatory discussions for prompt crafting, streamlining the translation of the discussions into visuals. Effective landscape planning relies on community insights through participatory design to achieve local needs. Visual media can assist community engagement, and visuals created using generative AI text-to-image models are increasingly adopted for such purposes. We explore a new approach of including generative images in participatory planning through a case study with the Diverse Corn Belt Project in the US Corn Belt. Our method is applicable to other contexts, and adds to the literature in three ways. First, we propose a compromise between real-time image generation and extended time workflows of translating participatory discussions into generative images, benefiting from the instant generation of generative models while controlling the output. Building on this proposed pace, we suggest creating what we call ‘controlled imperfect’ images as a balance between “fake perfects” and “conversational imperfects” suggested by the literature. In addition, we propose simplifying the process of translating participatory discussions into an image output through directly collecting keywords necessary for prompt engineering. We build on our case study to outline a revised method for future research.