📜 This is the official code repository for the pre-print titled 'CC-GPX: Extracting High-Quality Annotated Geospatial Data from Common Crawl' and published on arXiv: https://arxiv.org/abs/2405.11039

Authors: Ilya Ilyankou, Meihui Wang, Dr James Haworth and Dr Stefano Cavazzi

The Common Crawl (CC) corpus is the largest open web crawl dataset containing 9.5+ petabytes of data captured since 2008. The dataset is instrumental in training large language models, and as such it has been studied for (un)desirable content, and distilled for smaller, domain-specific datasets. However, to our knowledge, no research has been dedicated to using CC as a source of annotated geospatial data. In this paper, we introduce an efficient pipeline to extract annotated user-generated tracks from GPX files found in CC, and the resulting multimodal dataset with 1,416 pairings of human-written descriptions and MultiLineString vector data from the 6 most recent CC releases. The dataset can be used to study people's outdoor activity patterns, the way people talk about their outdoor experiences, and for developing trajectory generation or track annotation models.

We recommend running the notebooks in a separate virtual environment. Using conda,

# Navigate to the project folder
cd cc-gpx

# Create a new virtual environment
conda env create -f environment.yml

# Activate that new virtual environment
conda activate cc-gpx

# Run Jupyter (will open in your default browser)
jupyter lab

Run the notebooks in order to build the final GeoPackage dataset with the following fields:

If you find this dataset or workflow useful for your research, please cite us!

@article{ilyankou2024ccgpx,
      title={CC-GPX: Extracting High-Quality Annotated Geospatial Data from Common Crawl}, 
      author={Ilyankou, Ilya and Wang, Meihui and Haworth, James and Cavazzi, Stefano},
      year={2024},
      journal={arXiv preprint arXiv:2405.11039},
}