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Short Abstract

'Lost at Night' is a citizen science platform that georeferences astronaut photos of Earth's night lights. It creates a night global map to analyze light pollution and its ecological impacts, supporting the EU PLAN-B project.

Abstract

The systematic monitoring of Artificial Light at Night (ALAN) is predominantly conducted using satellite platforms such as DMSP-OLS and VIIRS-DNB. However, the extensive archive of high-resolution, multispectral (color) digital photographs captured by astronauts aboard the International Space Station (ISS) offers a complementary dataset with superior spatial resolution in urban centers. The primary limitation of this archive for scientific application is the lack of systematic georectification. This research addresses this data gap through a citizen science initiative designed to study the documented impacts of ALAN on biodiversity and human health.

The Lost at Night (lostatnight.org) platform, developed within the Horizon Europe project PLAN-B, functions as a Volunteered Geographic Information (VGI) system to process this imagery. The project engages the public in the manual georectification of the astronaut photography archive. Participants are presented with an image and an interactive map interface to identify and annotate ground control points (GCPs), such as coastal features, road intersections, and urban structures. This crowdsourcing approach leverages human pattern recognition to overcome challenges inherent to automated registration, including oblique viewing angles and atmospheric distortion.

The primary output is a longitudinal, open-access database of georectified nocturnal imagery. This process transforms qualitative visual imagery into quantitative, spatially-explicit data on nighttime radiance and spectral composition. The resulting dataset enables large-scale correlative studies between ALAN metrics and ecological indicators, directly supporting the objectives of the PLAN-B project. This presentation will detail the methodological framework, data validation protocols, and the application of this novel dataset for research into the spatio-temporal dynamics of light pollution and its environmental consequences.