Geobacter generates useful location embeddings on demand, it is an implementation of the Loc2Vec blog post from sentiance
A resnet is trained to embed renderings of geolocations using the triplet loss. Samples are generated based on the principle that:
"Everything is related to everything else, but near things are more related than distant things"
| Anchor | Positive | Negative |
|---|---|---|
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Initialise the open street map tile volumes and server
docker volume create openstreetmap-data
docker volume create openstreetmap-rendered-tiles
docker run \
-e THREADS=12 \
-v $PWD/data/osm/luxembourg-latest.osm.pbf:/data.osm.pbf \
-v openstreetmap-data:/var/lib/postgresql/12/main \
overv/openstreetmap-tile-server \
importexport PYTHONPATH=$PYTHONPATH:$PWD/geobacterCreate a python environment (for training)
pipenv install --dev
pipenv shellCreate a python environment (for inference)
pipenv install
pipenv shellStart the open street map tile server
docker-compose upInitialise some training and testing samples (which also caches tiles)
python bin/generate_samples.py --sample-count 100000 --buffer 100 --distance 500 --seed 1 --path data/extents/train_100000.json
python bin/generate_samples.py --sample-count 10000 --buffer 100 --distance 500 --seed 2 --path data/extents/test_10000.jsonpython -m geobacter.train(optional) Check that the open street map tile server is up
curl localhost:8080/tile/16/33879/22296.png --output test.pngStart the python service
export GEOBACTER_TOKEN=<token>
gunicorn -b 0.0.0.0:8000 --workers 4 --timeout 10 geobacter.inference.api:app(optional) Get the embedding for Notre-Dame
curl "localhost:8000/embeddings?lat=49.609598&lon=6.131606&token=<token>" | jq{
"embeddings": [
0.12629294395446777,
0.5683436393737793,
0.9822958111763,
0.38620898127555847,
-1.2079272270202637,
0.16978177428245544,
-0.3008042275905609,
0.06522990763187408,
0.5405853390693665,
-0.8018991947174072,
0.42124632000923157,
0.6691603064537048,
-0.40959250926971436,
-0.18567749857902527,
-0.017753595486283302,
0.3173545002937317
],
"checkpoint": "checkpoints/ResNetTriplet-OsmTileDataset-e393fd34-aa3c-4743-b270-e7f0d895b0a8_embedding_41450.pth",
"lon": 6.131606,
"lat": 49.609598,
"image_url": "image?lon=6.131606,lat=49.609598,token=<token>"
}
Semantically similar locations are embedded together
The embedding space can be interpolated
Similar locations can be queried
Use the api to characterise a pre-created route.
examples/api.pyUse a checkpoint to characterise a large number of samples.
examples/checkpoint.py