highway2vec

Kacper Leśniara and Piotr Szymański

Representing OpenStreetMap microregions with respect to their road network characteristics

Paper: https://github.com/Calychas/highway2vec/blob/master/SIGSPATIAL_2022_paper_262.pdf

This is a companion repository to the paper accepted at The 5th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI ’22).

📝 Repository status

This Github repository is no longer maintained. If you're interested in highway2vec or geospatial data in general, we encourage you to take a look at Spatial Representations for AI.
It implements highway2vec along with other models and utilities for working with geospatial data. The new highway2vec pipeline, which uses SRAI and Kedro is available here. If you have any feature request or a bug to report, feel free to open an issue in the SRAI repo.

Reference:

Kacper Leśniara and Piotr Szymański. 2022. highway2vec - representing OpenStreetMap microregions with respect to their road network charac- teristics. In The 5th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI ’22) (GeoAI ’22), November 1, 2022, Seattle, WA, USA. ACM, New York, NY, USA, 12 pages. https://doi.org/10. 1145/3557918.3565865

Steps to run

• pip install -r requirements.txt
• python scripts/download_and_preprocess_data.py
  This will download the data for selected cities and preprocess it (see data/generated).
• python scripts/generate_dataset.py
  This will generate dataset from the preprocessed data (see data/features).
• Run notebooks/vis_data.ipynb, which will generate data visualizations (see reports/figures)
• Run notebooks/autoencoder.ipynb, which will train the model run the inference (see data/runs/<run_name>).
• Run notebooks/vis_ae.ipynb, which will generate the analyses and visualizations of the generated embeddings (see data/runs/<run_name>/vis).

Final run used in the paper is available here