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.

通过深度学习对时空数据进行不确定性量化，可以解决在高风险决策应用中出现的预测错误问题，并为未来的研究方向提供了潜在的机会。

时空数据的深度学习不确定性量化：挑战与机遇

Uncertainty Quantification of Deep Learning for Spatiotemporal Data:  Challenges and Opportunities

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.

使用 GPS、遥感和计算模拟等技术，收集大量来自地球科学、农业、智能城市和公共安全等领域的时空数据，结合深度学习技术，开创解决以往无法解决的问题的新机遇并提出未来的研究需求。

深度学习在时空大数据上的应用：机遇与挑战展望

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

Decision-makers in GIS need to combine a series of spatial algorithms and
operations to solve geospatial tasks. For example, in the task of facility
siting, the Buffer tool is usually first used to locate areas close or away
from some specific entities; then, the Intersect or Erase tool is used to
select candidate areas satisfied multiple requirements. Though professionals
can easily understand and solve these geospatial tasks by sequentially
utilizing relevant tools, it is difficult for non-professionals to handle these
problems. Recently, Generative Pre-trained Transformer (e.g., ChatGPT) presents
strong performance in semantic understanding and reasoning. Especially, AutoGPT
can further extend the capabilities of large language models (LLMs) by
automatically reasoning and calling externally defined tools. Inspired by these
studies, we attempt to lower the threshold of non-professional users to solve
geospatial tasks by integrating the semantic understanding ability inherent in
LLMs with mature tools within the GIS community. Specifically, we develop a new
framework called GeoGPT that can conduct geospatial data collection,
processing, and analysis in an autonomous manner with the instruction of only
natural language. In other words, GeoGPT is used to understand the demands of
non-professional users merely based on input natural language descriptions, and
then think, plan, and execute defined GIS tools to output final effective
results. Several cases including geospatial data crawling, spatial query,
facility siting, and mapping validate the effectiveness of our framework.
Though limited cases are presented in this paper, GeoGPT can be further
extended to various tasks by equipping with more GIS tools, and we think the
paradigm of "foundational plus professional" implied in GeoGPT provides an
effective way to develop next-generation GIS in this era of large foundation
models.

利用 GeoGPT 将自然语言需求转化为 GIS 工具执行的方式可以降低非专业用户解决地理空间问题的门槛，我们展示了在多个任务中使用 GeoGPT 的有效性，同时该框架可以通过集成更多工具来扩展其适用范围。