In the geospatial domain, universal representation models are significantly less prevalent
than their extensive use in natural language processing and computer vision. This …

Time series forecasting is a critical task that provides key information for decision-making
across various fields. Recently, various fundamental deep learning architectures such as …

Spatiotemporal traffic data (STTD) displays complex correlational structures. Extensive
advanced techniques have been designed to capture these structures for effective …

The rapid advancement of foundation modelslarge-scale neural networks trained on
diverse, extensive datasetshas revolutionized artificial intelligence, enabling unprecedented …

Urban time series, such as mobility flows, energy consumption, and pollution records,
encapsulate complex urban dynamics and structures. However, data collection in each city …

Existing masked image modeling (MIM) methods mainly fill and enhance images by
modeling and filling masked areas. These techniques usually borrow principles from …