Urban streetscape evaluation is evolving from objective, semantic-based analysis to perception-driven approaches that better reflect how people experience city environments. Traditional computer vision captures physical features like greenery and buildings but often ignores perceptual factors such as lighting and weather. This study proposes a new framework that uses generative AI and large-scale human feedback to assess perceptual responses to weather-altered street view images. Using the TSIT model, 88,000 images were transformed to simulate sunny, cloudy, and rainy conditions, with 38,525 participants providing subjective ratings across 22 dimensions. Results show that weather strongly impacts perception: sunny scenes scored highest in beauty, cleanliness, and openness, while rainy scenes rated highest in negative attributes like “boring” and “depressing.” Some qualities, like “greenery” and “interesting,” remained consistent. The framework offers a scalable tool for urban planning and inclusive design by capturing diverse perceptual responses. Future work may consider factors like time of day, air quality, and pedestrian activity to further enrich urban visual analysis.