The poster will be presented at ECCV on Friday, October 4th, 10:30-12:30 at poster board #139.

1. Installation
2. Dataset
3. Training
4. Evaluation (includes pretrained weights)
5. Examples: A photo album that contains >2000 randomly chosen street-view images and corresponding predictions from our model.

1. Install Jax with GPU support: https://jax.readthedocs.io/en/latest/installation.html
2. Clone this repository:

   git clone https://github.com/fferflo/statewide-visual-geolocalization
   cd statewide-visual-geolocalization

3. Install the remaining dependencies:

   pip install -r requirements.txt

We train and evaluate our method on street-view images from the Mapillary platform and aerial imagery from Massachusetts, Washington DC, North Carolina (states in the US) and Berlin-Brandenburg, NRW, Saxony (states in Germany).

Please follow these instructions to download the data.

1. Fill in the dataset paths indicated by TODO in the configuration file config/main.yaml. The entries should look something like this:

   train:
     list:
       - tiles-path: .../data/opennrw
       path: .../data/mapillary-opennrw
   
   test:
     path: .../data/mapillary-boston100km2
     tiles:
       - path: .../data/massgis/utm19
     geojson: .../data/boston100km2.geojson

2. Run the training script:

   python3 train.py --output .../train --config config/main.yaml

   The results will be stored in .../train-YYYY-MM-DDTHH-mm-ss. The training uses all available GPUs by default. A training run with 2xH100 takes about 2.5 days.

1. Create a reference database for a search region by running the following script:

   python create_reference_database.py --train .../train-YYYY-MM-DDTHH-mm-ss --output .../refdb-massgis --tiles .../data/massgis/utm19 .../data/massgis/utm18

   This will create a division of the region into cells, predict embeddings for all cells, create a FAISS index for efficient retrieval and store everything in the output directory. This might take several days depending on your hardware setup and search region size.

   By default, the search region is defined to cover all tiles that are specified in --tiles. The argument accepts multiple tile datasets, such as the overlapping UTM18 and UTM19 regions of Massachusetts. Optionally, a geojson file can be passed to the script via --geojson to define a custom search region as a subset of the region covered by the tiles.

   Pretrained weights can be used by cloning the repository from Huggingface

   git clone https://huggingface.co/fferflo/statewide-geoloc-nomassgis

   and passing the path to the --train argument. These are not the original weights used in the paper, but are retrained using this repository. The results are slightly better than reported in the paper (see below).

   The output folder will contain the files:

   aerial_features.bin         # Embeddings for all cells
   cellregion.npz              # Division of the region into cells
   faiss.index                 # FAISS index that can be loaded via faiss.read_index("faiss.index")
   config.yaml                 # Configuration parameters of the search region, model, etc
   model_weights.safetensors   # Model weights used to create the embeddings
   

2. Localize query images against the reference database by running the following script:

   python localize.py --query .../data/mapillary-boston100km2 --reference .../refdb-massgis --stride 1

   This will predict embeddings for all street-view photos in the given dataset, and localize them against the reference database. The --stride parameter can be used to localize only a subset of the images (e.g. every 10th image with --stride 10).

   The script will print the Recall@k<r of the localization for different radii r and top-k cells. For example, the pretrained weights from above yield the following results:

   > python localize.py --query .../data/mapillary-massgis --reference .../refdb-massgis --stride 100
   
   ... takes some time ...
   
   Recall@1<0m: 0.2880
   Recall@5<0m: 0.5007
   Recall@10<0m: 0.5516
   Recall@50<0m: 0.6432
   Recall@100<0m: 0.6771
   
   Recall@1<25m: 0.4683
   Recall@5<25m: 0.6495
   Recall@10<25m: 0.6914
   Recall@50<25m: 0.7659
   Recall@100<25m: 0.7915
   
   Recall@1<50m: 0.6105
   Recall@5<50m: 0.7229
   Recall@10<50m: 0.7578
   Recall@50<50m: 0.8216
   Recall@100<50m: 0.8430
   
   Recall@1<100m: 0.6297
   Recall@5<100m: 0.7385
   Recall@10<100m: 0.7732
   Recall@50<100m: 0.8369
   Recall@100<100m: 0.8583
   

Please consider citing our work if you use the code or data, or build upon the ideas presented in the paper:

@inproceedings{fervers2024statewide,
  title     = {Statewide Visual Geolocalization in the Wild},
  author    = {Florian Fervers and Sebastian Bullinger and Christoph Bodensteiner and Michael Arens and Rainer Stiefelhagen},
  booktitle = {ECCV},
  year      = {2024}
}

Feel free to open an issue in this Github repository if you have any problems with the code or data.