# Deep Learning for Spatiotemporal Big Data: A Vision on Opportunities and Challenges

arXiv: https://arxiv.org/abs/2310.19957 · HTML: https://mlsanity.com/paper/2310.19957/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 39 references that are in the map.
- First posted on arXiv 30 Oct 2023.

## Builds on (most cited first, 9 of 9)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [Meta-Transfer Learning for Few-Shot Learning](https://mlsanity.com/paper/1812.02391/) (2018)
- [FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2208.05419/) (2022)
- [A Survey on Uncertainty Quantification Methods for Deep Learning](https://mlsanity.com/paper/2302.13425/) (2023)
- [GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations](https://mlsanity.com/paper/2202.08956/) (2022)
- [A 20-Year Community Roadmap for Artificial Intelligence Research in the US](https://mlsanity.com/paper/1908.02624/) (2019)

## Built on by (most cited first, 1 of 1)

- [RAST: A Retrieval Augmented Spatio-Temporal Framework for Traffic Prediction](https://mlsanity.com/paper/2508.16623/) (2025)

## Abstract (arXiv)

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.