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 emerging spatiotemporal big data assets, together with
the recent progress of deep learning technologies, provide unique opportunities
to transform society. However, it is widely recognized that deep learning
sometimes makes unexpected and incorrect predictions with unwarranted
confidence, causing severe consequences in high-stake decision-making
applications (e.g., disaster management, medical diagnosis, autonomous
driving). Uncertainty quantification (UQ) aims to estimate a deep learning
model's confidence. This paper provides a brief overview of UQ of deep learning
for spatiotemporal data, including its unique challenges and existing methods.
We particularly focus on the importance of uncertainty sources. We identify
several future research directions for spatiotemporal data.