This research introduces a novel Soundscape-to-Image Diffusion model, a generative AI model, to visualize street soundscapes. The model links auditory and visual perceptions of place, addressing a gap in geographic studies that typically prioritize visual data. By creating audio-image pairs, the model translates acoustic environments into visual representations. The researchers evaluate the model using both machine and human-based methods, demonstrating its ability to generate recognizable street scenes based on sound alone, thus highlighting the significant visual information contained within soundscapes. This work bridges the gap between AI and human geography, offering potential applications in urban design and environmental psychology. The model's success underscores the importance of considering multiple sensory inputs for understanding huma ...