Day-to-Night Street View Image Generation for 24-Hour Urban Scene Auditing Using Generative AI

Zhiyi Liu, Tingting Li, Tianyi Ren, Da Chen, Wenjing Li, Waishan Qiu

Research output: Contribution to journalArticlepeer-review


A smarter city should be a safer city. Nighttime safety in metropolitan areas has long been a global concern, particularly for large cities with diverse demographics and intricate urban forms, whose citizens are often threatened by higher street-level crime rates. However, due to the lack of night-time urban appearance data, prior studies based on street view imagery (SVI) rarely addressed the perceived night-time safety issue, which can generate important implications for crime prevention. This study hypothesizes that night-time SVI can be effectively generated from widely existing daytime SVIs using generative AI (GenAI). To test the hypothesis, this study first collects pairwise day-and-night SVIs across four cities diverged in urban landscapes to construct a comprehensive day-and-night SVI dataset. It then trains and validates a day-to-night (D2N) model with fine-tuned brightness adjustment, effectively transforming daytime SVIs to nighttime ones for distinct urban forms tailored for urban scene perception studies. Our findings indicate that: (1) the performance of D2N transformation varies significantly by urban-scape variations related to urban density; (2) the proportion of building and sky views are important determinants of transformation accuracy; (3) within prevailed models, CycleGAN maintains the consistency of D2N scene conversion, but requires abundant data. Pix2Pix achieves considerable accuracy when pairwise day–and–night-night SVIs are available and are sensitive to data quality. StableDiffusion yields high-quality images with expensive training costs. Therefore, CycleGAN is most effective in balancing the accuracy, data requirement, and cost. This study contributes to urban scene studies by constructing a first-of-its-kind D2N dataset consisting of pairwise day-and-night SVIs across various urban forms. The D2N generator will provide a cornerstone for future urban studies that heavily utilize SVIs to audit urban environments.

Original languageEnglish
Article number112
JournalJournal of Imaging
Issue number5
Early online date7 May 2024
Publication statusPublished - 31 May 2024

Data Availability Statement

Data, models, or codes supporting this study’s findings are available from the corresponding author upon reasonable request.


  • day-to-night
  • generative AI
  • night scene
  • nighttime perception
  • street view imagery

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering

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