Generative Adversarial Networks (GANs) have promoted a variety of applications in
computer vision and natural language processing, among others, due to its generative …

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 …

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 …

Public events, such as concerts and sports games, can be major attractors for large crowds,
leading to irregular surges in travel demand. Accurate human mobility prediction for public …

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 …

Recently, an abundant amount of urban vehicle trajectory data has been collected in road
networks. Many studies have used machine learning algorithms to analyze patterns in …

Generating trajectory data is among promising solutions to addressing privacy concerns,
collection costs, and proprietary restrictions usually associated with human mobility …

The prevalence of ubiquitous location-aware devices and mobile Internet enables us to
collect massive individual-level trajectory dataset from users. Such trajectory big data bring …

We present a new method to create spatial data using a generative adversarial network
(GAN). Our contribution uses coarse and widely available geospatial data to create maps of …