CC-GPX: Extracting High-Quality Annotated Geospatial Data from Common Crawl
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915982251917312 |
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| author | Ilyankou, Ilya Wang, Meihui Cavazzi, Stefano Haworth, James |
| author_facet | Ilyankou, Ilya Wang, Meihui Cavazzi, Stefano Haworth, James |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_11039 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CC-GPX: Extracting High-Quality Annotated Geospatial Data from Common Crawl Ilyankou, Ilya Wang, Meihui Cavazzi, Stefano Haworth, James Computation and Language 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 |
| title | CC-GPX: Extracting High-Quality Annotated Geospatial Data from Common Crawl |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2405.11039 |