4 citations · 4 across the 4 of their papers we have counts for
4 papers
UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
Dahai Yu, Dingyi Zhuang, Lin Jiang +5
Spatiotemporal prediction plays a critical role in numerous real-world applications such as urban planning, transportation optimization, disaster response, and pandemic control. In…
Multi-Task Dense Prediction Fine-Tuning with Mixture of Fine-Grained Experts
Yangyang Xu, Xi Ye, Duo Su
Multi-task learning (MTL) for dense prediction has shown promising results but still faces challenges in balancing shared representations with task-specific specialization. In this…
ST-GraphNet: A Spatio-Temporal Graph Neural Network for Understanding and Predicting Automated Vehicle Crash Severity
Mahmuda Sultana Mimi, Md Monzurul Islam, Anannya Ghosh Tusti +2
Understanding the spatial and temporal dynamics of automated vehicle (AV) crash severity is critical for advancing urban mobility safety and infrastructure planning. In this work,…
The Role of Open-Source LLMs in Shaping the Future of GeoAI
Xiao Huang, Zhengzhong Tu, Xinyue Ye +1
Large Language Models (LLMs) are transforming geospatial artificial intelligence (GeoAI), offering new capabilities in data processing, spatial analysis, and decision support. This…