Street Space Quality Improvement: Fusion of Subjective Perception in Street View Image Generation

Abstract

The development of sustainable cities and communities aligns with the Sustainable Development Goals (SDGs) and smart city initiatives, emphasizing the integration of residents' subjective perceptions into urban street space planning. While previous research has quantitatively assessed streetscape quality, existing methods remain largely conceptual and lack actionable strategies for improvement. Recent advances in generative AI have enabled the generation of realistic and visually compelling images across various domains. However, most existing image generation frameworks lack a mechanism to directly incorporate residents' subjective perceptions when modifying street view imagery. This gap results in generated images that, while aesthetically impressive, may not fully align with the preferences and lived experiences of local communities. To address this issue, we propose a novel, data-driven approach that conditionally fuses subjective perception data into the transformation of original street view images. Our method integrates multidimensional perception cues, including beautiful, safety, lively, etc., fused the 8.8 million perception survey data to generate street views that are more reflective of public sentiment. Experimental evaluations demonstrate an 86.36% success rate in enhancing 22 distinct subjective perception metrics based on initial street view inputs. This fusion-based methodology advances both image generation and smart city development by aligning generated landscapes with resident preferences. It also provides urban planners and community stakeholders with a robust framework for visualizing targeted street space improvements and designing more livable, human-centric urban environments.