CC-GPX: Extracting High-Quality Annotated Geospatial Data from Common Crawl

TL;DR

This paper presents a pipeline to extract high-quality, annotated geospatial data from the Common Crawl dataset, creating a multimodal dataset for outdoor activity analysis and trajectory modeling.

Contribution

It introduces a novel method for extracting and annotating user-generated geospatial tracks from web crawl data, filling a gap in available datasets.

Findings

Created a dataset with 1,416 track-description pairs from CC

Demonstrated the dataset's potential for outdoor activity and trajectory research

Provided reproducible code for data extraction and annotation

Abstract

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, as well as for developing trajectory generation or track annotation models, or for various other problems in place of synthetically generated routes. Our reproducible code is available on GitHub: https://github.com/ilyankou/cc-gpx