Light pollution affects 83% of the global population, yet comprehensive monitoring remains lim- ited 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 subcon- tinent. 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 per- formance 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 VIIRS satellite radiance (60,681 grid points), 734 Unihedron SQM readings, and 236 Globe at Night observations. Separately, VIIRS-derived Bortle esti- mates showed only 44.1% agreement with Globe at Night human observers, highlighting the limitations of satellite-only mapping for ground-level sky quality assessment.
Suhani Gupta (Mon,) studied this question.