JDC

Journal of Discovery Core

JDC

Digital Intelligence Press

Hong KongHKhttps://dipscie.com/index.php/JDC/index

3135-7687

Articles

<bold>Deep Learning-Driven Generative Layout Design Model for Sensory Garden Environment Design</bold>

Zhang

Jing

outlook_8A9201AAC2554F63@outlook.com

College of Arts, Shandong Jianzhu University, Jinan, Shandong, China

01 09 2026

1 37

Received: 3062026

2026

Jing Zhang (Author)

<a rel="license" href="https://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" src="//i.creativecommons.org/l/by/4.0/88x31.png" /></a><p>This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p>

Deep learning; Diffusion model; Generative design; Sensory garden; Design of environment

[1] He, M., Wang, Y., Wang, W. J., & Xie, Z. (2022). Therapeutic plant landscape design of urban forest parks based on the Five Senses Theory: A case study of Stanley Park in Canada. International Journal of Geoheritage and Parks, 10(1), 97-112. https://doi.org/10.1016/j.ijgeop.2022.02.004

[2] Ren, Q., Weng, Y., & Hu, Z. (2025). A study of space creation for healing landscape design in the post-epidemic era. Frontiers in Psychology, 16, 1618451. https://doi.org/10.3389/fpsyg.2025.1618451

[3] Shi, Y., Liu, S., Huang, X., & Sheng, Q. (2025). Roaming through perished gardens: the application of space syntax in the reconstruction of the 17th-century Zhi Garden. Humanities and Social Sciences Communications, 12(1), 1508. https://doi.org/10.1057/s41599-025-05789-6

[4] Yan, A., & Cheng, Z. (2024). A review of the development and future challenges of case-based reasoning. Applied Sciences, 14(16), 7130. https://doi.org/10.3390/app14167130

[5] Zheng, X., Huang, Y., Liu, Y., Zhang, Z., Li, Y., & Yan, H. (2025). Financing mode and scheme decision support for large urban rail transit projects: a revised case-based reasoning approach. Engineering, Construction and Architectural Management, 32(12), 8494-8523. https://doi.org/10.1108/ECAM-03-2023-0202

[6] Wang, Y., Zhou, Z., Tan, X., Pan, Y., Yuan, J., Qiu, Z., & Liu, C. (2024). Unveiling the potential of progressive training diffusion model for defect image generation and recognition in industrial processes. Neurocomputing, 592, 127837. https://doi.org/10.1016/j.neucom.2024.127837

[7] Singh, A., Chatta, N. K., Vagula, Y., Ehtesham, A., Kumar, S., & Talaei Khoei, T. (2026). A Taxonomy of Generative Models with a Focus on Diffusion Models and Denoising Techniques. Electronics, 15(6), 1293. https://doi.org/10.3390/electronics15061293

[8] Liang, L., Miao, B., Wang, X., Akhtar, N., Vice, J., & Mian, A. (2026). CymbaDiff: Structured Spatial Diffusion for Sketch-based 3D Semantic Urban Scene Generation. Advances in Neural Information Processing Systems, 38, 81114-81138. https://doi.org/10.48550/arXiv.2510.13245

[9] Zhou, Y., Leng, H., Meng, S., Wu, H., & Zhang, Z. (2024). StructDiffusion: End-to-end intelligent shear wall structure layout generation and analysis using diffusion model. Engineering Structures, 309, 118068. https://doi.org/10.1016/j.engstruct.2024.118068

[10] Zeng, P., Yin, J., Huang, Y., Zhong, J., & Lu, S. (2025, October). AI-based generation and optimization of energy-efficient residential layouts controlled by contour and room number. In Building Simulation (Vol. 18, No. 10, pp. 2777-2805). Beijing: Tsinghua University Press. https://doi.org/10.1007/s12273-025-1337-4

[11] Li, M. R., Cao, Y., & Li, G. W. (2023). An approach to developing and protecting linear heritage tourism: The construction of cultural heritage corridor of traditional villages in Mentougou District using GIS. International Journal of Geoheritage and Parks, 11(4), 607-623. https://doi.org/10.1016/j.ijgeop.2023.11.002

[12] Meng, L., Zhang, B., & Cao, L. (2026). Establishing linear cultural heritage corridors by integrating cultural and ecological values: A case study of the Jinzhong section of the Great Tea Road. Land, 15(2), 293. https://doi.org/10.3390/land15020293

[13] Guo, X., Fu, S., & Zhu, D. (2025). Aesthetic quality evaluation of packaging design with graph neural networks and composition features. Scientific Reports, 15(1), 36046. https://doi.org/10.1038/s41598-025-20046-1

[14] Yuan, Y., Liu, F., Yang, G., & Wang, M. (2026). A review of representation and similarity measurement methods for geospatial scenes. International Journal of Geographical Information Science, 40(5), 1519-1546. https://doi.org/10.1080/13658816.2025.2563697

[15] Karimi, K. (2023). The configurational structures of social spaces: Space syntax and urban morphology in the context of analytical, evidence-based design. Land, 12(11), 2084. https://doi.org/10.3390/land12112084

[16] Waldner, F., Diakogiannis, F. I., Batchelor, K., Ciccotosto-Camp, M., Cooper-Williams, E., Herrmann, C., ... & Toovey, A. (2021). Detect, consolidate, delineate: Scalable mapping of field boundaries using satellite images. Remote sensing, 13(11), 2197. https://doi.org/10.3390/rs13112197

[17] Shu, D., Zhang, Z., Wan, F., Ru, W., Yang, B., Zhang, Y., ... & Chen, X. (2025). SatViT-Seg: A transformer-only lightweight semantic segmentation model for real-time land cover mapping of high-resolution remote sensing imagery on satellites. Remote Sensing, 18(1), 1. https://doi.org/10.3390/rs18010001

[18] Wang, Y., Zhang, W., Chen, W., & Chen, C. (2024). BSDSNet: Dual-stream feature extraction network based on segment anything model for synthetic aperture radar land cover classification. Remote Sensing, 16(7), 1150. https://doi.org/10.3390/rs16071150

[19] Yu, Z., Li, Y., Xiao, J., Zhou, H., & Lin, B. (2025). Physical embedding on building surface spatial relationship shows better performance on graph-based daylight prediction. Building and Environment, 114141. https://doi.org/10.1016/j.buildenv.2025.114141

[20] Han, J., Lu, X. Z., & Lin, J. R. (2025). Pretrained graph neural network for embedding semantic, spatial, and topological data in building information models. Computer‐Aided Civil and Infrastructure Engineering, 40(26), 4607-4631. https://doi.org/10.1111/mice.70073

[21] Vrahatis, A. G., Lazaros, K., & Kotsiantis, S. (2024). Graph attention networks: a comprehensive review of methods and applications. Future Internet, 16(9), 318. https://doi.org/10.3390/fi16090318

[22] Li, Z. P., Wang, S. G., Zhang, Q. H., Pan, Y. J., Xiao, N. A., Guo, J. Y., ... & Huang, D. S. (2024). Graph pooling for graph-level representation learning: a survey. Artificial Intelligence Review, 58(2), 45. https://doi.org/10.1007/s10462-024-10949-2

[23] Ma, W., Jiang, Q., Wang, Q., Yu, D., Huang, Y., He, B., & Jin, X. (2025). Ycsc-unet: A y-shaped composite spatial channel network based on u-net for breast lesion ultrasound image segmentation. Neurocomputing, 131865. https://doi.org/10.1016/j.neucom.2025.131865

[24] Yang, F., & Wang, B. (2024). Dual Channel‐Spatial Self‐Attention Transformer and CNN synergy network for 3D medical image segmentation. Applied Soft Computing, 167, 112255. https://doi.org/10.1016/j.asoc.2024.112255

[25] Chen, Y., Huang, Q., Geng, M., Wang, Z., & Han, Y. (2025). A systematic review on cell nucleus instance segmentation. IET Image Processing, 19(1), e70129. https://doi.org/10.1049/ipr2.70129

[26] Luo, J. (2026). Design and Processing of Benevolent Image Symbols Based on Computer Graphics and Visual Communication. International Journal of Image and Graphics, 2750105. https://doi.org/10.1142/s0219467827501051