Large pre-trained models, also known as foundation models (FMs), are trained in a task-
agnostic manner on large-scale data and can be adapted to a wide range of downstream …

Urban Geography studies forms, social fabrics, and economic structures of cities from a
geographic perspective. Catalysed by the increasingly abundant spatial big data, Urban …

ABSTRACT A common need for artificial intelligence models in the broader geoscience is to
encode various types of spatial data, such as points, polylines, polygons, graphs, or rasters …

Geo-tagged images are publicly available in large quantities, whereas labels such as object
classes are rather scarce and expensive to collect. Meanwhile, contrastive learning has …

Generating learning-friendly representations for points in space is a fundamental and long-
standing problem in machine learning. Recently, multi-scale encoding schemes (such as …

Large pre-trained models, also known as foundation models (FMs), are trained in a task-
agnostic manner on large-scale data and can be adapted to a wide range of downstream …

We present the hierarchical graph infomax (HGI) approach for learning urban region
representations (vector embeddings) with points-of-interest (POIs) in a fully unsupervised …

The field of Artificial Intelligence (AI) can be roughly divided into two branches: Symbolic
Artificial Intelligence and Connectionist Artificial Intelligence (or so-called Subsymbolic AI) …

The prevalence of location-based services contributes to the explosive growth of individual-
level trajectory data and raises public concerns about privacy issues. In this research, we …

We present a novel approach for estimating the proportional distributions of function types
(ie functional distributions) in an urban area through learning semantics preserved …