Code for CVPR 2024 paper "EarthLoc: Astronaut Photography Localization by Indexing Earth from Space". The paper introduces the task of Astronaut Photography Localization (APL) through image retrieval.

[ArXiv] [BibTex]

Clone the repo, install packages, and download the queries and database images as such

git clone https://github.com/gmberton/EarthLoc
cd EarthLoc
pip install -r requirements.txt

python data/database_downloader.py
python data/queries_downloader.py

This will download all required images within the directory data. Each image filename contains its metadata, according to this format: @ lat1 @ lon1 @ lat2 @ lon2 @ lat3 @ lon3 @ lat4 @ lon4 @ image_id @ timestamp @ nadir_lat @ nadir_lon @ sq_km_area @ orientation @ .jpg

Where the first 8 fields are the latitudes and longitudes of the 4 corners of the image (i.e. the footprint). nadir_lat and nadir_lon are the position of nadir (which corresponds to the center of the footprint in database images, but can be thousands of kilometers aways from the footprint for queries).

For database images, image_id corresponds to zoom, row, column (according to WMTS). For query images, image_id corresponds to mission, roll, frame, which are a unique identifier of ISS photographs.

sq_km_area is the footprint covered area in squared kilometers, and orientation is the orientation of the image from 0 to 360° (e.g. 0° means that the image is north-up, like a normal map): orientation is always 0° for database images.

After the images are downloaded, download the file containing the intersections between queries and database images (it would be too heavy to compute online at the beginning of every experiment) at this link and put it in data/queries_intersections_with_db_2021.torch. This file used for inference, to see if the predictions are correct.

Once the dataset is downloaded, simply run

python train.py

or

python train.py -h

to see the possible hyperparameters.

Available here

Here is the bibtex to cite our paper

@InProceedings{Berton_CVPR_2024_EarthLoc,
    author    = {Berton, Gabriele and Stoken, Alex and Caputo, Barbara and Masone, Carlo},
    title     = {EarthLoc: Astronaut Photography Localization by Indexing Earth from Space},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
}