A vast amount of location information exists in unstructured texts, such as social media
posts, news stories, scientific articles, web pages, travel blogs, and historical archives …

We propose a geographically reproducible approach to urban scene sensing based on
large-scale pre-trained models. With the rise of GeoAI research, many high-quality urban …

Large language models (LLMs) exhibit impressive capabilities across diverse tasks in
natural language processing. Nevertheless, challenges arise such as large model …

The concept of “human as sensors” defines a new sensing model, in which humans act as
sensors by contributing their observations, perceptions, and sensations. This is crucial for …

While utilizing Twitter data for crisis management is of interest to different response
authorities, a critical challenge that hinders the utilization of such data is the scarcity of …

Intelligent machines have grown in importance in recent years in object recognition in terms
of their ability to envision, comprehend, and reach decisions. There are a lot of complicated …

Natural language texts, such as tweets and news, contain a vast amount of geospatial
information, which can be extracted by first recognizing toponyms in texts (toponym …

Nature disasters play a key role in sha** human-urban infrastructure interactions. Effective
and efficient response to natural disasters is essential for building resilience and sustainable …

Toponym recognition is used to extract toponyms from natural language texts, which is a
fundamental task of ubiquitous geographic information applications. Existing toponym …

Toponym resolution is crucial for extracting geographic information from natural language
texts, such as social media posts and news articles. Despite the advancements in current …