Deep Learning for Spatiotemporal Big Data: A Vision on Opportunities and
  Challenges
Zhe Jiang
With advancements in GPS, remote sensing, and computational simulation, an
enormous volume of spatiotemporal data is being collected at an increasing
speed from various application domains, spanning Earth sciences, agriculture,
smart cities, and public safety. Such emerging geospatial and spatiotemporal
big data, coupled with recent advances in deep learning technologies, foster
new opportunities to solve problems that have not been possible before. For
instance, remote sensing researchers can potentially train a foundation model
using Earth imagery big data for numerous land cover and land use modeling
tasks. Coastal modelers can train AI surrogates to speed up numerical
simulations. However, the distinctive characteristics of spatiotemporal big
data pose new challenges for deep learning technologies. This vision paper
introduces various types of spatiotemporal big data, discusses new research
opportunities in the realm of deep learning applied to spatiotemporal big data,
lists the unique challenges, and identifies several future research needs.