The urban greening paradox: Measuring day-night perceived safety divergences from paired street view images using generative AI

Whether nighttime is the better half of life varies across cities depending on how safe residents perceive their neighborhood environments to be. Although street view imagery (SVI) is instrumental in auditing perceived safety, city-wide nighttime SVI does not exist. Consequently, the extent to which day-night safety perceptions diverge remains unclear. Although emerging studies have used day-to-night (D2N) translations to generate nighttime SVIs, the model convergence and transferability are unknown. Using paired day-night SVIs from U.S. and Chinese cities, we confirmed the converging sample size (∼2000 pairs) and demonstrated both cross-density and cross-cultural transferability of the D2N model. Medium-density SVIs achieved the best cross-density transferability, while models trained on U.S. samples outperformed those trained on Chinese samples, indicating moderate concerns about the adequacy of cross-cultural training data for applying D2N models across regions. Moreover, interpretable machine learning reveals a pronounced day–night asymmetry in perceptual mechanisms: daytime safety perception is primarily shaped by pedestrian-oriented configurations, whereas nighttime safety perception relies more on visibility-related cues. Meanwhile, brightness emerges as a key positive predictor in both periods. Additionally, urban greening intensifies day–night divergence in perceived safety disproportionately rather than uniformly enhancing perceived safety. This pattern illustrates the urban greening paradox: trees and plants increase daytime perceived safety, yet are linked to lower perceived safety after dark. Lastly, city-scale mapping in Boston reveals salient spatial heterogeneity: central and northwest corridors retain relatively high safety perception after dark, while southern areas experience the steepest nighttime declines. Our scalable GenAI framework extends urban scene studies to the nighttime domain and enables city-wide mapping of perceived safety after dark, informing more inclusive nighttime urban planning.