Quantifying Geospatial in the Common Crawl Corpus

Large language models (LLMs) exhibit emerging geospatial capabilities, stemming from their pre-training on vast unlabelled text datasets that are often derived from the Common Crawl corpus. However, the geospatial content within CC remains largely unexplored, impacting our understanding of LLMs' spatial reasoning. This paper investigates the prevalence of geospatial data in recent Common Crawl releases using Gemini, a powerful language model. By analyzing a sample of documents and manually revising the results, we estimate that between 1 in 5 and 1 in 6 documents contain geospatial information such as coordinates and street addresses. Our findings provide quantitative insights into the nature and extent of geospatial data within Common Crawl, and web crawl data in general. Furthermore, we formulate questions to guide future investigations into the geospatial content of available web crawl datasets and its influence on LLMs.

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

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. 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.

Do Sentence Transformers Learn Quasi-Geospatial Concepts from General Text?

Sentence transformers are language models designed to perform semantic search. This study investigates the capacity of sentence transformers, fine-tuned on general question-answering datasets for asymmetric semantic search, to associate descriptions of human-generated routes across Great Britain with queries often used to describe hiking experiences. We find that sentence transformers have some zero-shot capabilities to understand quasi-geospatial concepts, such as route types and difficulty, suggesting their potential utility for routing recommendation systems.