Health sensitive decision-making for integrating artificial intelligence and landscape architecture in urban cities
DOI: https://doi.org/10.3846/jeelm.2026.25254Abstract
Urban landscape architecture design plays an essential role in public health because it is influenced by several factors such as social connection, safety, walkability and amenities access. Traditional planning techniques are time-consuming and often result in suboptimal or aesthetically incoherent layout decisions, which diminish the accessibility and safety of landscape design. The research issue is addressed by integrating artificial intelligence (AI) techniques in architectural design to improve the overall aesthetic design while handling health decisions. This study uses Generative Adversarial Networks (GAN) and Reinforcement Learning (RL) to satisfy the health objectives via landscape layouts. The GAN uses the generator and discriminator to design the landscape layout that covers inputs such as functional requirements, site dimension, zone restrictions and health design principles. The combination of generator and discriminator helps to maximize the outcomes in injury prevention, mental restoration and physical activities. The generated layouts are further explored using RL rewards regarding usage, safety, and access, ensuring the layout appearance and aesthetics are directly tailored to health. The incorporation of new technology into landscape architecture can offer evidence-based approaches for constructing salutogenic landscapes. The scalable computational technique enables faster scenario evaluation, providing information for informed planning policies. Then, the excellence of the landscape layout is evaluated using experimental results.
Keywords:
urban landscape design, landscape management, Artificial Intelligence, Generative Adversarial Networks (GAN) and Reinforcement Learning (RL), health outcomesHow to Cite
Share
License
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Allahyar, M., & Kazemi, F. (2021). Effect of landscape design elements on promoting neuropsychological health of children. Urban Forestry & Urban Greening, 65, Article 127333. https://doi.org/10.1016/j.ufug.2021.127333
Arifuzzaman, M., Gazder, U., Islam, M. S., & Mamun, A. A. (2020). Prediction and sensitivity analysis of CNTs-modified asphalt’s adhesion force using a radial basis neural network model. Journal of Adhesion Science and Technology, 34(10), 1100–1114.
Bharmoria, R., & Sharma, V. (2023, December). Urban sustainable intervention to address the physical factor for degradation of visual place quality in the hilly urban region: A case of Manali town, Himachal Pradesh. In IOP Conference Series: Earth and Environmental Science (Vol. 1279, No. 1, p. 012015). IOP Publishing. https://doi.org/10.1088/1755-1315/1279/1/012015
Bibri, S. E., Krogstie, J., Kaboli, A., & Alahi, A. (2024). Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review. Environmental Science and Ecotechnology, 19, Article 100330. https://doi.org/10.1016/j.ese.2023.100330
Casali, Y., Aydin, N. Y., & Comes, T. (2022). Machine learning for spatial analyses in urban areas: A scoping review. Sustainable Cities and Society, 85, Article 104050. https://doi.org/10.1016/j.scs.2022.104050
Chen, X. (2023). Environmental landscape design and planning system based on computer vision and deep learning. Journal of Intelligent Systems, 32(1), Article 20220092. https://doi.org/10.1515/jisys-2022-0092
Chen, Y., Wang, X., & Zhang, C. (2022). Wavelet transform-based 3D landscape design and optimization for digital cities. International Journal of Antennas and Propagation, 2022, Article 184198. https://doi.org/10.1155/2022/1184198 (Retraction published 2023, International Journal of Antennas and Propagation, 2023, Article 9781290)
El Alaoui, M., & Rougui, M. (2024). Examining the Application of Artificial Neural Networks (ANNs) for advancing energy efficiency in building: A comprehensive reviews. Journal of Sustainability Research, 6(1), Article e240001. https://doi.org/10.20900/jsr20240001
He, J. (2022). Landscape design method of urban wetland park using the building information model. Wireless Communications and Mobile Computing, 2022(1), Article 6228513. https://doi.org/10.1155/2022/6228513
Huang, Y., Li, W., Tian, F., & Meng, X. (2020). A fitness landscape ruggedness multiobjective differential evolution algorithm with a reinforcement learning strategy. Applied Soft Computing, 96, Article 106693. https://doi.org/10.1016/j.asoc.2020.106693
Jabbar, M., Yusoff, M. M., & Shafie, A. (2022). Assessing the role of urban green spaces for human well-being: A systematic review. GeoJournal, 87, 4405–4423. https://doi.org/10.1007/s10708-021-10474-7
Jahani, A., & Rayegani, B. (2020). Forest landscape visual quality evaluation using artificial intelligence techniques as a decision support system. Stochastic Environmental Research and Risk Assessment, 34(10), 1473–1486. https://doi.org/10.1007/s00477-020-01832-x
Jahani, A., & Saffariha, M. (2020). Aesthetic preference and mental restoration prediction in urban parks: An application of environmental modeling approach. Urban Forestry & Urban Greening, 54, Article 126775. https://doi.org/10.1016/j.ufug.2020.126775
Jia, F. (2022). Neural network model of urban landscape design based on multi-target detection. Computational Intelligence and Neuroscience, 2022, Article 9383273. https://doi.org/10.1155/2022/9383273
Mishra, H. S., Bell, S., Vassiljev, P., Kuhlmann, F., Niin, G., & Grellier, J. (2020). The development of a tool for assessing the environmental qualities of urban blue spaces. Urban Forestry & Urban Greening, 49, Article 126575. https://doi.org/10.1016/j.ufug.2019.126575
Moravec, V., Hynek, N., Gavurova, B., & Kubak, M. (2024). Everyday artificial intelligence unveiled: Societal awareness of technological transformation. Oeconomia Copernicana, 15(2), 367–406. https://doi.org/10.24136/oc.2961
Olszewska-Guizzo, A., Sia, A., & Escoffier, N. (2023). Revised Contemplative Landscape Model (CLM): A reliable and valid evaluation tool for mental health-promoting urban green spaces. Urban Forestry & Urban Greening, 86, Article 128016. https://doi.org/10.1016/j.ufug.2023.128016
Ou, B. (2021). How landscape architects achieve sustainability by using public parks and public parks contribution to sustainable urban development [Doctoral dissertation, University of Illinois at Urbana-Champaign]. https://www.ideals.illinois.edu/items/118720
Pala, D., Caldarone, A. A., Franzini, M., Malovini, A., Larizza, C., Casella, V., & Bellazzi, R. (2020). Deep learning to unveil correlations between urban landscape and population health. Sensors, 20(7), Article 2105. https://doi.org/10.3390/s20072105
Pelau, C., Dabija, D.-C., & Stanescu, M. (2024). Can I trust my AI friend? The role of emotions, feelings of friendship and trust for consumers’ information-sharing behavior toward AI. Oeconomia Copernicana, 15(2), 407–433. https://doi.org/10.24136/oc.2916
Pena, M. L. C., Carballal, A., Rodríguez-Fernández, N., Santos, I., & Romero, J. (2021). Artificial intelligence applied to conceptual design. A review of its use in architecture. Automation in Construction, 124, Article 103550. https://doi.org/10.1016/j.autcon.2021.103550
Reyes-Riveros, R., Altamirano, A., De La Barrera, F., Rozas-Vásquez, D., Vieli, L., & Meli, P. (2021). Linking public urban green spaces and human well-being: A systematic review. Urban Forestry & Urban Greening, 61, Article 127105. https://doi.org/10.1016/j.ufug.2021.127105
Senem, M. O., Koç, M., Tunçay, H. E., & As, I. (2023). Using deep learning to generate front and backyards in landscape architecture. Architecture and Planning Journal (APJ), 28(3), Article 1. https://doi.org/10.54729/2789-8547.1196
Silva, B. N., Khan, M., Jung, C., Seo, J., Muhammad, D., Han, J., & Han, K. (2018). Urban planning and smart city decision management empowered by real-time data processing using big data analytics. Sensors, 18(9), Article 2994. https://doi.org/10.3390/s18092994
Szpilko, D., Naharro, F. J., Lăzăroiu, G., Nica, E., & Gallegos, A. d. l. T. (2023). Artificial intelligence in the smart city – a literature review. Engineering Management in Production and Services, 15(4), 53–75. https://doi.org/10.2478/emj-2023-0028
Wen, L., Kenworthy, J., & Marinova, D. (2020). Higher density environments and the critical role of city streets as public open spaces. Sustainability, 12(21), Article 8896. https://doi.org/10.3390/su12218896
Wu, A. N., Stouffs, R., & Biljecki, F. (2022). Generative Adversarial Networks in the built environment: A comprehensive review of the application of GANs across data types and scales. Building and Environment, 223, Article 109477. https://doi.org/10.1016/j.buildenv.2022.109477
Yu, S., Guan, X., Zhu, J., Wang, Z., Jian, Y., Wang, W., & Yang, Y. (2023). Artificial intelligence and urban green space facilities optimization using the LSTM model: Evidence from China. Sustainability, 15(11), Article 8968. https://doi.org/10.3390/su15118968
Zhang, C. (2020). Design and application of fog computing and Internet of Things service platform for smart city. Future Generation Computer Systems, 112, 630–640. https://doi.org/10.1016/j.future.2020.06.016
View article in other formats
CrossMark check
Published
Issue
Section
Copyright
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.