Landscape design concept generation combining cultural mapping technology and multimodal modeling

Landscape design is a complex and multifaceted task traditionally carried out by humans, which can lead to inefficiencies and limited creativity in the design process. Automated methods that have been used to perform landscape design till now are limited by their creativity, flexibility and their ability to balance between the structural design and the preferences. In this paper, we present a new landscape design model which utilizes sketch-based generation and multimodal learning using both text descriptions and initial sketches to generate high-quality images of landscape designs. Our model utilizes a deep learning model containing a Pix2Pix GAN model which generates images from sketches and a CLIP model which aligns the images with the text descriptions to meet the needs of both the structural requirements and aesthetic preferences. Our model gives a more creative, flexible and detailed solution to automated landscape design compared with traditional methods. Our experiments show that our model generates higher quality images compared with existing methods, such as Pix2Pix and CycleGAN, and better aligns the images with the textual descriptions. The quantitative and qualitative analysis further validates that our model is effective in generating innovative and aesthetically pleasing landscape images. Our model provides an innovative solution to automated landscape design which is more efficient, creative and contextually relevant. By utilizing our model, designers are able to generate more efficient, innovative and contextually relevant designs. With further improvements, our model will be able to better process complex and abstract design requirements towards the development of intelligent design systems.