Metric learning aims to measure the similarity among samples while using an optimal
distance metric for learning tasks. Metric learning methods, which generally use a linear …

Spatial-temporal prediction is a fundamental problem for constructing smart city, which is
useful for tasks such as traffic control, taxi dispatching, and environment policy making. Due …

The outbreak and spread of COVID-19 remind us again of the devastating attack that human-
to-human transmitted respiratory infectious diseases (H-HRIDs) bring to global economics …

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

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

Quantifying the topological similarities of different parts of urban road networks enables us to
understand urban growth patterns. Although conventional statistics provide useful …

Most studies of urban morphology rely on morphometrics, such as building area and street
length. However, these methods often fall short in capturing visual patterns that carry …

Crime prediction plays an impactful role in enhancing public security and sustainable
development of urban. With recent advances in data collection and integration technologies …

The proliferation of geospatial data in urban and territorial environments has significantly
facilitated the development of geospatial artificial intelligence (GeoAI) across various urban …

Trajectory similarity is a fundamental operation in spatial-temporal data mining with wide-
ranging applications. However, trajectories inherently exhibit diversity due to varied …