Deep Learning for Spatiotemporal Big Data: A Vision on Opportunities and Challenges
It identifies opportunities and challenges for researchers in geospatial and spatiotemporal domains, but it is incremental as it synthesizes existing ideas rather than proposing new solutions.
This vision paper explores how deep learning can address problems in fields like Earth sciences and smart cities by leveraging spatiotemporal big data, but it highlights challenges such as data characteristics without presenting specific results or numbers.
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.