46 citations · 56 across the 6 of their papers we have counts for
6 papers
STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19
Han Bao, Xun Zhou, Yiqun Xie +2
Human mobility estimation is crucial during the COVID-19 pandemic due to its significant guidance for policymakers to make non-pharmaceutical interventions. While deep learning app…
Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation
Yan Li, Mingzhou Yang, Matthew Eagon +4
The eco-toll estimation problem quantifies the expected environmental cost (e.g., energy consumption, exhaust emissions) for a vehicle to travel along a path. This problem is impor…
Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability
Zhexiong Liu, Licheng Liu, Yiqun Xie +2
Spatio-temporal machine learning is critically needed for a variety of societal applications, such as agricultural monitoring, hydrological forecast, and traffic management. These…
Modeling Reservoir Release Using Pseudo-Prospective Learning and Physical Simulations to Predict Water Temperature
Xiaowei Jia, Shengyu Chen, Yiqun Xie +4
This paper proposes a new data-driven method for predicting water temperature in stream networks with reservoirs. The water flows released from reservoirs greatly affect the water…
Statistically-Robust Clustering Techniques for Mapping Spatial Hotspots: A Survey
Yiqun Xie, Shashi Shekhar, Yan Li
Mapping of spatial hotspots, i.e., regions with significantly higher rates of generating cases of certain events (e.g., disease or crime cases), is an important task in diverse soc…
Towards Spatial Variability Aware Deep Neural Networks (SVANN): A Summary of Results
Jayant Gupta, Yiqun Xie, Shashi Shekhar
Spatial variability has been observed in many geo-phenomena including climatic zones, USDA plant hardiness zones, and terrestrial habitat types (e.g., forest, grasslands, wetlands,…