Health sensitive decision-making for integrating artificial intelligence and landscape architecture in urban cities

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.