AI Empowering Urban Landscape Art Design: Ecological Adaptation and Intelligent Optimization Practice

Traditional landscape design often relies on subjective intuition, and it is difficult to cope with complex climate factors and high-density environmental challenges. Most of the existing AI applications remain at the conceptual rendering level, lacking the deep coupling of ecological rationality, artistic sensibility, and engineering feasibility. This study puts forward a landscape design framework driven by artificial intelligence-double helix model of ecology and art, aiming at solving the contradiction between ecological fragmentation and public demand expansion in high-density urban built-up areas. This paper collects data by deploying multi-source heterogeneous sensor networks, and uses CFD simulation, multi-objective optimization algorithm, and StyleGAN3 to realize the dual-channel generation of ecological adaptability and regional aesthetic grammar. This study promotes the paradigm shift of landscape design from static experience-oriented to dynamic data-driven and provides a reproducible path for the sustainable renewal of high-density urban environment.