Citizen Science to Assess Light Pollution with Mobile Phones

TL;DR

This paper demonstrates that citizen-collected smartphone photos can effectively classify streetlight colors, providing a scalable, cost-effective method to study light pollution and complement existing data sources.

Contribution

It introduces a novel citizen science approach for assessing streetlight color using smartphones, enabling large-scale, affordable data collection for light pollution analysis.

Findings

Smartphone photos can accurately classify streetlight colors.

Citizen science data complements official databases.

The method enables large-scale, cost-effective light pollution studies.

Abstract

The analysis of the colour of artificial lights at night has an impact on diverse fields, but current data sources have either limited resolution or scarce availability of images for a specific region. In this work, we propose crowdsourced photos of streetlights as an alternative data source: for this, we designed NightUp Castelldefels, a pilot for a citizen science experiment aimed at collecting data about the colour of streetlights. In particular, we extract the colour from the collected images and compare it to an official database, showing that it is possible to classify streetlights according to their colour from photos taken by untrained citizens with their own smartphones. We also compare our findings to the results obtained from one of the current sources for this kind of study. The comparison highlights how the two approaches give complementary information about artificial lights at night in the area. This work opens a new avenue in the study of the colour of artificial lights at night with the possibility of accurate, massive and cheap data collection.