Urban Landscape Design Optimization and Smart Planning Framework Based on Big Data Analytics

This article mainly explores landscape design optimization based on big data (BD) in order to improve the aesthetics, functionality, and residents' satisfaction with the urban landscape. To achieve this goal, an algorithm that integrates deep learning (DL) and multi-objective optimization is developed. By iteratively adjusting parameters, the model generates a range of innovative landscape design schemes, which are verified through a practical case. To assess the scheme's effect, this article adopts a combination of quantitative and qualitative analyses, including landscape aesthetics score, space utilization ratio, residents' satisfaction survey, and expert review. Experimental results show that the optimized landscape design scheme has significantly improved aesthetic effect, space utilization efficiency, and residents' satisfaction. Specifically, the average score of landscape aesthetics has increased by 20%, the space utilization rate has increased by 15%, and the residents' satisfaction with the new design scheme is as high as 85%.