SkyQI: A Citizen Science Platform for Global Light Pollution Monitoring Using Smartphone Photography and Computer Vision

v2 (2026-05-11): Validation statistics recomputed against real EOG VNL V2.2 2025 satellite data, replacing interpolated estimates used in v1. Updated values: 84,100 grid cells, SkyQI–VIIRS correlation r = -0.175 (n=103, p=0.074), VIIRS-vs-Globe-at-Night agreement 51.7% (n=238), mean error -2.53 Bortle classes. Bortle accuracy (65.4%) and all algorithm details unchanged. Light pollution affects 83% of the global population, yet comprehensive monitoring remains limited by the cost of specialized equipment. This paper presents SkyQI, a citizen science platform that enables light pollution monitoring using smartphone photography and computer vision. The platform is designed for global use, but the present evaluation is geographically limited to the Indian subcontinent. The system employs connected-component based star detection, regional brightness gradient analysis, and color temperature assessment to estimate approximate SQM-equivalent values and Bortle Dark-Sky Scale classifications from uncalibrated photographs. Evaluation on 104 geolocated night-sky photographs demonstrates 65.4% accuracy (±1 Bortle class, 95% CI: 55.8%–74.2%), with strongest performance for dark sky verification (Bortle 1–2: 75.8%) and urban documentation (Bortle 7–9: 100.0%, n=16), though mid-range classification (Bortle 3–6) remains challenging at 9.1%. Performance is contextualized against external reference data from the EOG VNL V2.2 2025 VIIRS satellite composite (84,100 grid cells at 0.1°), 734 Unihedron SQM readings, and 238 Globe at Night observations. Separately, VIIRS-derived Bortle estimates showed only 51.7% agreement with Globe at Night human observers (mean error −2.53 Bortle classes), highlighting the limitations of satellite-only mapping for ground-level sky quality assessment.