(Street) Lights Will Guide You: Georeferencing Nighttime Astronaut Photography of Earth

Alex Stoken, Peter Ilhardt, Mark Lambert, Kenton Fisher; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 492-501

Astronaut photography from the International Space Station provides the highest spatial resolution nighttime Earth observations imagery publicly available offering up to a 150x increase in resolution over other freely accessible satellite data sources. Yet this imagery is underutilized in science applications because it lacks the geolocation metadata required for downstream analysis. We present NightMatch a fast and accurate method for localizing and georectifying nighttime astronaut photography. By combining street network data with daytime satellite imagery we produce a reliable reference target for similarity detection via pairwise image matching. We curate and release the Astronaut Imagery Matching Subset - Night (AIMS-Night) a collection of 363 images and ground truth localizations and benchmark our method against this set to establish a robust localization pipeline. Our method correctly localizes 81.8% of AIMS-Night and can be quickly deployed on the over 2 million nighttime astronaut photographs to produce a high quality analysis-ready data product.