Unist: A prompt-empowered universal model for urban spatio-temporal prediction
Urban spatio-temporal prediction is crucial for informed decision-making, such as traffic
management, resource optimization, and emergence response. Despite remarkable …
management, resource optimization, and emergence response. Despite remarkable …
Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlook
As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for
sustainable development by harnessing the power of cross-domain data fusion from diverse …
sustainable development by harnessing the power of cross-domain data fusion from diverse …
Heterogeneous contrastive learning for foundation models and beyond
In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive
self-supervised learning to model large-scale heterogeneous data. Many existing foundation …
self-supervised learning to model large-scale heterogeneous data. Many existing foundation …
Self-supervised Learning for Geospatial AI: A Survey
The proliferation of geospatial data in urban and territorial environments has significantly
facilitated the development of geospatial artificial intelligence (GeoAI) across various urban …
facilitated the development of geospatial artificial intelligence (GeoAI) across various urban …
Hawkes-enhanced spatial-temporal hypergraph contrastive learning based on criminal correlations
Crime prediction is a crucial yet challenging task within urban computing, which benefits
public safety and resource optimization. Over the years, various models have been …
public safety and resource optimization. Over the years, various models have been …
A Unique Framework of Heterogeneous Augmentation Graph Contrastive Learning for Both Node and Graph Classification
Q Shao, D Chen, W Yu - IEEE Transactions on Network …, 2024 - ieeexplore.ieee.org
Graph contrastive learning has gained significant attention for its effectiveness in leveraging
unlabeled data and achieving superior performance. However, prevalent graph contrastive …
unlabeled data and achieving superior performance. However, prevalent graph contrastive …
An Event-centric Framework for Predicting Crime Hotspots with Flexible Time Intervals
J **, Y Hong, G Xu, J Zhang, J Tang… - arxiv preprint arxiv …, 2024 - arxiv.org
Predicting crime hotspots in a city is a complex and critical task with significant societal
implications. Numerous spatiotemporal correlations and irregularities pose substantial …
implications. Numerous spatiotemporal correlations and irregularities pose substantial …
Self-Supervised Masked Hypergraph Autoencoders for Spatio-Temporal Forecasting
Y Huang, N **ao - 2024 International Joint Conference on …, 2024 - ieeexplore.ieee.org
Spatio-temporal forecasting has become a critical research area with various applications in
modern urban environments. Recent works have employed spatial-temporal graph neural …
modern urban environments. Recent works have employed spatial-temporal graph neural …
High-Performance Spatio-Temporal Information Mixer for Traffic Forecasting
Y Huang, N **ao - 2024 International Joint Conference on …, 2024 - ieeexplore.ieee.org
Traffic forecasting is a critical task in the field of Intelligent Transportation Systems. Previous
research in traffic forecasting has primarily focused on integrating Graph Neural Networks …
research in traffic forecasting has primarily focused on integrating Graph Neural Networks …
Marine Traffic Risk Assessment Using Spatio Temporal AIS Data in Makassar Port, Indonesia
JRK Bokau, F Saransi - … Conference of Inland Water and Ferries …, 2024 - books.google.com
Marine Traffic Risk Assessment Using Spatio Temporal AIS Data in Makassar Port, Indonesia
Page 158 Check for updates Marine Traffic Risk Assessment Using Spatio Temporal AIS Data …
Page 158 Check for updates Marine Traffic Risk Assessment Using Spatio Temporal AIS Data …