Artificial intelligence in sustainable landscape design: Utilizing recycled waste materials

Despite tremendous progress in the field of landscape design, there is still a lack of artificial intelligence (AI) integration in order to maximize the utilization of recovered waste materials for sustainable practices. The majority of recent study examines AI in landscape design for practical and esthetic reasons, with a few studies focusing on sustainability. In a similar vein, research on recycled waste materials in landscape design highlights the advantages for the environment but falls short of AI's level of accuracy and optimization. Our study closes this gap by utilizing AI to improve the efficacy and efficiency of recycled waste materials in landscape design. By providing scalable and repeatable solutions, this interdisciplinary approach has the potential to raise the bar for sustainable landscape design. This study was to use recycled waste materials and AI in sustainable landscape design. According to data gathered from Guangzhou, as land use stockpile enters a new phase, it is imperative to unleash this potential through urban ruins (URs) reuse. To determine which features have the greatest influence on the landscape design process make sure it is appropriate for evaluating index. We proposed the spider monkey dove swarm optimized generative adversarial network (SMDSO-GAN) for sustainable landscape design utilizing recycled waste materials. To evaluate the suggested solution works in terms of accuracy rate, R2 and MAE. As a result, recycled waste materials demonstrated by the suggested superior performance over other similar models in terms of accuracy rate (98%), recycle management (93%), MAE (25%), and R2 32%.