Smartphone-Based Edge Intelligence for Nighttime Visibility Estimation in Smart Cities

Impaired visibility, a major global environmental threat, is a result of light scattering by atmospheric particulate matter. While digital photographs are increasingly used for daytime visibility estimation, such methods are largely ineffective at night owing to the different scattering effects. Here, we introduce an image-based algorithm for inferring nighttime visibility from a single photograph by analyzing the forward scattering index and optical thickness retrieved from glow effects around light sources. Using photographs crawled from social media platforms across mainland China, we estimated the nationwide visibility for one year using the proposed algorithm, achieving high goodness-of-fit values (R2 = 0.757; RMSE = 4.318 km), demonstrating robust performance under various nighttime scenarios. The model also captures both chronic and episodic visibility degradation, including localized pollution events. These results highlight the potential of using ubiquitous smartphone photography as a low-cost, scalable, and real-time sensing solution for nighttime atmospheric monitoring in urban areas.