Humans subconsciously engage in geospatial reasoning when reading articles. We
recognize place names and their spatial relations in text and mentally associate them with …

We present a multi-level geocoding model (MLG) that learns to associate texts to geographic
locations. The Earth's surface is represented using space-filling curves that decompose the …

We present a multi-level geocoding model (MLG) that learns to associate texts to geographic
coordinates. The Earth's surface is represented using space-filling curves that decompose …

Large language models (LLMs) have achieved great success in general domains of natural
language processing. In this paper, we bring LLMs to the realm of geoscience with the …

Named geographic entities (geo-entities for short) are the building blocks of many
geographic datasets. Characterizing geo-entities is integral to various application domains …

The application of machine learning (ML) in a range of geospatial tasks is increasingly
common but often relies on globally available covariates such as satellite imagery that can …

Large language models (LLMs) have achieved huge success for their general knowledge
and ability to solve a wide spectrum of tasks in natural language processing (NLP). Due to …

Large language models (LLMs) have shown remarkable capabilities across a broad range
of tasks involving question answering and the generation of coherent text and code …

Geocoding is the task of converting location mentions in text into structured data that
encodes the geospatial semantics. We propose a new architecture for geocoding, GeoNorm …

Geocoding aims to assign unambiguous locations (ie, geographic coordinates) to place
names (ie, toponyms) referenced within documents (eg, within spreadsheet tables or textual …