28 citations · 45 across the 12 of their papers we have counts for
4 papers · 1 filter
Spatial Knowledge-Infused Hierarchical Learning: An Application in Flood Mapping on Earth Imagery
Zelin Xu, Tingsong Xiao, Wenchong He +2
Deep learning for Earth imagery plays an increasingly important role in geoscience applications such as agriculture, ecology, and natural disaster management. Still, progress is of…
A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space
Wenchong He, Zhe Jiang, Tingsong Xiao +5
Transformers are widely used deep learning architectures. Existing transformers are mostly designed for sequences (texts or time series), images or videos, and graphs. This paper p…
Uncertainty Quantification of Deep Learning for Spatiotemporal Data: Challenges and Opportunities
Wenchong He, Zhe Jiang
With the advancement of GPS, remote sensing, and computational simulations, large amounts of geospatial and spatiotemporal data are being collected at an increasing speed. Such eme…
A Survey on Uncertainty Quantification Methods for Deep Learning
Wenchong He, Zhe Jiang, Tingsong Xiao +2
Deep neural networks (DNNs) have achieved tremendous success in computer vision, natural language processing, and scientific and engineering domains. However, DNNs can make unexpec…