(Street) Lights Will Guide You: Georeferencing Nighttime Astronaut Photography of Earth
Abstract
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 meta-data required for downstream analysis. We present Night-Match, a fast and accurate method for localizing and geo-rectifying 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.
Cite
Text
Stoken et al. "(Street) Lights Will Guide You: Georeferencing Nighttime Astronaut Photography of Earth." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024. doi:10.1109/CVPRW63382.2024.00054Markdown
[Stoken et al. "(Street) Lights Will Guide You: Georeferencing Nighttime Astronaut Photography of Earth." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024.](https://mlanthology.org/cvprw/2024/stoken2024cvprw-street/) doi:10.1109/CVPRW63382.2024.00054BibTeX
@inproceedings{stoken2024cvprw-street,
title = {{(Street) Lights Will Guide You: Georeferencing Nighttime Astronaut Photography of Earth}},
author = {Stoken, Alex and Ilhardt, Peter and Lambert, Mark and Fisher, Kenton},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2024},
pages = {492-501},
doi = {10.1109/CVPRW63382.2024.00054},
url = {https://mlanthology.org/cvprw/2024/stoken2024cvprw-street/}
}