Multi-modal deep learning for intelligent landscape design generation: A novel CBS3-LandGen model

Mingzhen Lu and Lili Shi

PLOS ONE, 2025, vol. 20, issue 7, 1-29

Abstract: With the acceleration of the global urbanization process, landscape design is facing increasingly complex challenges. Traditional manual design methods are gradually unable to meet the needs for efficiency, precision, and sustainability. To address this issue, this paper proposes an intelligent landscape design generation model based on multimodal deep learning, namely CBS3-LandGen. By integrating image data, text data, and generation optimization techniques, this model can generate landscape plans that meet the design objectives within limited time and resources.Specifically, the model employs the ConvNeXt network to process image data, uses the BART model to analyze text information, and optimizes the generation effect through StyleGAN3. This multimodal architecture enables the model to perform excellently in terms of image generation quality, text generation consistency, and the fusion of images and text. In the experiments, we trained and tested the model using the DeepGlobe and COCO datasets. The results show that on the DeepGlobe dataset, the Frechet Inception Distance (FID) is 25.5 and the Inception Score (IS) is 4.3; on the COCO dataset, the FID is 30.2 and the IS is 4.0. These results demonstrate the superiority of CBS3-LandGen in generation tasks, especially in aspects such as image quality, diversity, and multimodal data fusion. The method proposed in this paper provides new ideas for intelligent landscape design and promotes the integration of landscape design and deep learning technologies. Future research will further optimize the model’s performance, improve training efficiency, and expand its application potential in practical landscape design, urban planning, ecological protection, and other fields. The code for implementing CBS3-LandGen Model is available at https://github.com/LMZ81/CBS3-LandGen.git.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0328138

DOI: 10.1371/journal.pone.0328138

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