AI Article Synopsis
- The evolution of cities is heavily influenced by urban landscape design, and advancements in AI have made it easier for people to navigate and interact with these spaces.
- A new model called URDDL uses deep learning to create urban landscape designs automatically, taking into account land use, road conditions, and emotional responses to environments.
- Experimental results show that URDDL significantly reduces errors compared to traditional design methods, providing a promising future for automated landscape design.
Article Abstract
The evolution of a city is significantly shaped by the design of its urban landscape. The advancement of artificial intelligence has substantially increased convenience for individuals. This research proposes an urban landscape layout model powered by artificial intelligence that automatically generates urban landscape design based on deep learning (URDDL) with two dimensions: emotional tendency and urban landscape appraisal. The input image represents land use and surrounding road conditions, while the output image depicts the selection of the main entrance and the internal spatial function layout. The Pix2Pix model is trained to learn the internal function layout based on varying land use and road conditions. Additionally, a domain-specific dictionary is constructed using an existing semantic resource vocabulary, where positive and negative sentiment words are compared with their corresponding sentiment values, focusing on categories such as Stimulate, Sense, and Action. Experimental results indicate that the absolute average error of the URDDL model is 91.31%, with a maximum error of 96.87%. The degree of fit is highly appropriate for evaluating the emotional prediction of urban landscapes. The findings demonstrate that the URDDL model outperforms traditional design methods regarding generated results, suggesting its potential for future applications in automated landscape design.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11623217 | PMC |
| http://dx.doi.org/10.7717/peerj-cs.2426 | DOI Listing |
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