Use location embeddings to enrich prediction mappings.
We represent locations by embedding tiles into a vector. This technique is also known as loc2vec and was first described in this blogpost. An implementation in pytorch is described here, with a corresponding GitHub repository. We provide a tensorflow impelmentation.
We generate tiles from the opentopomapp.
Latitude and longitude coordinates for a certain region can be found using
this tool.
Tiles are generated by running python src/downloader.py.
The zoom level depends on you use case.
The feature extractor consists of a pre-trained (imagenet) DenseNet121 followed by a 1x1 convolution layer, a dense layer and a final embedding layer (with linear activations).
The model can be trained using by running python src/main.py.
Results can be visualized by running jupyter notebook notebook/tensorboard_embedding_visualization.ipynb and
running all cells. Afterwards the projections can be visualized in Tensorboard by running
tensorboard --logdir logs/ in the terminal. Embeddings are found under the PROJECTOR tab.
Note: tensorboard projector documentation is written for tensorflow==1.*, whereas the model is trained in
tensorflow==2.1. As such, one will have to create a separate virtual environment for the visualization
(see requirements_tensorboard.txt)