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.
UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
Florida State University（佛罗里达州立大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Alabama, Tuscaloosa（阿拉巴马大学塔斯卡洛osa分校）；University of Florida（佛罗里达大学）
University of Science and Technology of China（中国科学技术大学）；Beijing Institute of Control Engineering（北京控制工程研究所）
The high dynamics and heterogeneous interactions in the complicated urban systems have raised the issue of uncertainty quantification in spatiotemporal human mobility, to support critical decision-makings in risk-aware web applications such as urban event prediction where fluctuations are of significant interests. Given the fact that uncertainty quantifies the potential variations around prediction results, traditional learning schemes always lack uncertainty labels, and conventional uncertainty quantification approaches mostly rely upon statistical estimations with Bayesian Neural Networks or…
Decision-Oriented Uncertainty Quantification for Risk Control in Earth System Spatiotemporal Foundation Models
Vanderbilt University（范德堡大学）；City University of New York（纽约城市大学）；Yale University（耶鲁大学）；Wyze Inc.（Wyze公司）；Northeastern University（东北大学）；University of Southern California（南加州大学）；Southwest University（西南大学）
University of Science and Technology of China（中国科学技术大学）；Huawei Technologies（华为技术有限公司）
Spatiotemporal forecasting is an imperative topic in data science due to its diverse and critical applications in smart cities. Existing works mostly perform consecutive predictions of following steps with observations completely and continuously obtained, where nearest observations can be exploited as key knowledge for instantaneous status estimation. However, the practical issues of early activity planning and sensor failures elicit a brand-new task, i.e., non-consecutive forecasting. In this paper, we define spatiotemporal learning systems with missing observation as Grey Spatiotemporal Sys…
Tackling Incomplete Data in Air Quality Prediction: A Bayesian Deep Learning Framework for Uncertainty Quantification
School of Intelligent Systems Engineering, Sun Yat-sen University（智能系统工程学院，中山大学）；Guangdong Provincial Key Laboratory of Intelligent Transportation System（广东省智能交通系统重点实验室）；Guangdong Provincial Engineering Research Center for Traffic Environmental Monitoring and Control（广东省交通环境监测与控制工程研究中心）
Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics
Data and Artificial Intelligence Cluster, Eindhoven University of Technology（埃因霍温理工大学数据与人工智能集群）；Swiss Plasma Center, École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院瑞士等离子体中心）
UC San Diego（加州大学圣地亚哥分校）；University of Washington（华盛顿大学）；Northeastern University（东北大学）
Deep learning is gaining increasing popularity for spatiotemporal forecasting. However, prior works have mostly focused on point estimates without quantifying the uncertainty of the predictions. In high stakes domains, being able to generate probabilistic forecasts with confidence intervals is critical to risk assessment and decision making. Hence, a systematic study of uncertainty quantification (UQ) methods for spatiotemporal forecasting is missing in the community. In this paper, we describe two types of spatiotemporal forecasting problems: regular grid-based and graph-based. Then we analyz…
Interpretable Time Series Autoregression for Periodicity Quantification
Department of Urban Studies and Planning, Massachusetts Institute of Technology（城市研究与规划系，麻省理工学院）；Questrom School of Business, Boston University（questrom商学院，波士顿大学）；Department of Mathematics at the University of California, San Diego（加州大学圣地亚哥分校数学系）
Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling
Faculty of Mechanical Engineering Delft University of Technology（代尔夫特理工大学机械工程学院）；School of Engineering Brown University（布朗大学工程学院）
What do data tell us about physics-and what don't they tell us? There has been a surge of interest in using machine learning models to discover governing physical laws such as differential equations from data, but current methods lack uncertainty quantification to communicate their credibility. This work addresses this shortcoming from a Bayesian perspective. We introduce a novel model comprising "leaf" modules that learn to represent distinct experiments' spatiotemporal functional data as neural networks and a single "root" module that expresses a nonparametric distribution over their governi…
Deep Learning for Spectrum Prediction in Cognitive Radio Networks: State-of-the-Art, New Opportunities, and Challenges
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学电子与信息工程学院）；Pillar of Information Systems Technology and Design, Singapore University of Technology and Design（新加坡科技设计大学信息系统技术与设计学院）
Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction
Stellenbosch University（斯泰伦博斯大学）；The Nature Conservancy（大自然保护协会）；University of Cape Town（开普敦大学）；Norwegian Institute for Nature Research—NINA（挪威自然研究所—NINA）；Jet Propulsion Laboratory（喷气推进实验室）；California Institute of Technology（加州理工学院）；South African Environmental Observation Network（南非环境观测网络）
Prediction of wind turbines power with physics-informed neural networks and evidential uncertainty quantification
University of Granada（格拉纳达大学）；Research Centre for Information and Communication Technologies (CITIC-UGR)（信息与通信技术研究中心（CITIC-UGR））；TECNALIA（泰克纳利亚研究中心）；Imperial College London（帝国理工学院）
A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimation
Institute of Photogrammetry and Remote Sensing (IPF), Karlsruhe Institute of Technology (KIT)（摄影测量与遥感研究所（卡尔斯鲁厄理工学院））；The Ohio State University（俄亥俄州立大学）
Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines, allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of interest. However, applications of deep learning in experimental domains are often limited by the out-of-distribution drift between the experiments, where the network trained for one set of imaging conditions becomes sub-optimal for different ones. This limitation is particularly stringent in the quest to have an automated experiment setting, where retraining or transfer learning becomes impr…
AODDiff: Probabilistic Reconstruction of Aerosol Optical Depth via Diffusion-based Bayesian Inference
State Key Laboratory of Fire Science（火灾科学国家重点实验室）；University of Science and Technology of China（中国科学技术大学）；Fire Research Division（火灾研究部）；National Institute of Standards and Technology（美国国家标准与技术研究院）
Argonne National Laboratory（阿贡国家实验室）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
Deep-learning-based data-driven forecasting methods have produced impressive results for traffic forecasting. A major limitation of these methods, however, is that they provide forecasts without estimates of uncertainty, which are critical for real-time deployments. We focus on a diffusion convolutional recurrent neural network (DCRNN), a state-of-the-art method for short-term traffic forecasting. We develop a scalable deep ensemble approach to quantify uncertainties for DCRNN. Our approach uses a scalable Bayesian optimization method to perform hyperparameter optimization, selects a set of hi…
University of Bath（巴斯大学）；Imago7 foundation（Imago7基金会）；University of Edinburgh（爱丁堡大学）；GE Healthcare（GE医疗）；Technical University of Munich（慕尼黑工业大学）
We propose a dictionary-matching-free pipeline for multi-parametric quantitative MRI image computing. Our approach has two stages based on compressed sensing reconstruction and deep learned quantitative inference. The reconstruction phase is convex and incorporates efficient spatiotemporal regularisations within an accelerated iterative shrinkage algorithm. This minimises the under-sampling (aliasing) artefacts from aggressively short scan times. The learned quantitative inference phase is purely trained on physical simulations (Bloch equations) that are flexible for producing rich training sa…
TrustEnergy: A Unified Framework for Accurate and Reliable User-level Energy Usage Prediction
Florida State University（佛罗里达州立大学）；Massachusetts Institute of Technology（麻省理工学院）；University of California, Santa Barbara（加州大学圣巴巴拉分校）；University of Florida（佛罗里达大学）
A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural Networks
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin（德克萨斯大学奥斯汀分校奥登计算工程与科学学院）；Computational Science Center, National Renewable Energy Laboratory（美国国家可再生能源实验室计算科学中心）
A plug-and-play approach with fast uncertainty quantification for weak lensing mass mapping
Université Caen Normandie, ENSICAEN, CNRS, Normandie Univ, GREYC UMR 6072, F-14000 Caen, France Universit\'e Paris-Saclay, Universit\'e Paris Cit\'e, CEA, CNRS, AIM, 91191, Gif-sur-Yvette, France Institutes of Computer Science；Astrophysics, Foundation for Research
With the advancement of GPS and remote sensing technologies, large amounts of geospatial and spatiotemporal data are being collected from various domains, driving the need for effective and efficient prediction methods. Given spatial data samples with explanatory features and targeted responses (categorical or continuous) at a set of locations, the problem aims to learn a model that can predict the response variable based on explanatory features. The problem is important with broad applications in earth science, urban informatics, geosocial media analytics and public health, but is challenging…
University of California, Berkeley（加州大学伯克利分校）；Global Change Institute, The University of Queensland（昆士兰大学全球变化研究所）
Quantification is the task of estimating the class-distribution of a data-set. While typically considered as a parameter estimation problem with strict assumptions on the data-set shift, we consider quantification in-the-wild, on two large scale data-sets from marine ecology: a survey of Caribbean coral reefs, and a plankton time series from Martha's Vineyard Coastal Observatory. We investigate several quantification methods from the literature and indicate opportunities for future work. In particular, we show that a deep neural network can be fine-tuned on a very limited amount of data (25 - …
University of Southern California（南加州大学）；Argonne National Laboratory（阿贡国家实验室）
Ptychography, as an essential tool for high-resolution and nondestructive material characterization, presents a challenging large-scale nonlinear and non-convex inverse problem; however, its intrinsic photon statistics create clear opportunities for statistical-based deep learning approaches to tackle these challenges, which has been underexplored. In this work, we explore normalizing flows to obtain a surrogate for the high-dimensional posterior, which also enables the characterization of the uncertainty associated with the reconstruction: an extremely desirable capability when judging the re…
Applying machine learning models to meteorological data brings many opportunities to the Geosciences field, such as predicting future weather conditions more accurately. In recent years, modeling meteorological data with deep neural networks has become a relevant area of investigation. These works apply either recurrent neural networks (RNN) or some hybrid approach mixing RNN and convolutional neural networks (CNN). In this work, we propose STConvS2S (Spatiotemporal Convolutional Sequence to Sequence Network), a deep learning architecture built for learning both spatial and temporal data depen…
Scientific Computing and Imaging Institute（科学计算与成像研究所）；University of Utah（犹他大学）；School of Computing（计算学院）
Clinical investigations of anatomy's structural changes over time could greatly benefit from population-level quantification of shape, or spatiotemporal statistic shape modeling (SSM). Such a tool enables characterizing patient organ cycles or disease progression in relation to a cohort of interest. Constructing shape models requires establishing a quantitative shape representation (e.g., corresponding landmarks). Particle-based shape modeling (PSM) is a data-driven SSM approach that captures population-level shape variations by optimizing landmark placement. However, it assumes cross-sectiona…
Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods
University of Maryland Baltimore County（马里兰大学巴尔的摩分校）；NSF HDR Institute for Harnessing Data and Model Revolution in the Polar Regions (iHARP)（NSF HDR极地地区数据与模型变革研究所）；University of North Texas（北得克萨斯大学）
Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-seq Data Analysis
School of Mathematical Sciences, Peking University（北京大学数学科学学院）；Center for Machine Learning Research, Peking University（北京大学机器学习研究中心）；Laboratory of Mathematics and Its Applications (LMAM), Peking University（北京大学数学及其应用实验室）；Center for Quantitative Biology, Peking University（北京大学定量生物学中心）；National Engineering Laboratory for Big Data Analysis and Applications, Peking University（北京大学大数据分析与应用国家工程实验室）；AI for Science Institute, Beijing（北京科学智能研究院）
West Virginia University（西弗吉尼亚大学）；Virginia Tech（弗吉尼亚理工大学）；U.S. Naval Research Laboratory（美国海军研究实验室）；Space Environments Technologies（空间环境技术公司）
Machine learning (ML) is often viewed as a black-box regression technique that is unable to provide considerable scientific insight. ML models are universal function approximators and - if used correctly - can provide scientific information related to the ground-truth dataset used for fitting. A benefit to ML over parametric models is that there are no predefined basis functions limiting the phenomena that can be modeled. In this work, we develop ML models on three datasets: the Space Environment Technologies (SET) High Accuracy Satellite Drag Model (HASDM) density database, a spatiotemporally…
Korea Advanced Institute of Science and Technology（韩国科学技术院）
A `trajectory' refers to a trace generated by a moving object in geographical spaces, usually represented by of a series of chronologically ordered points, where each point consists of a geo-spatial coordinate set and a timestamp. Rapid advancements in location sensing and wireless communication technology enabled us to collect and store a massive amount of trajectory data. As a result, many researchers use trajectory data to analyze mobility of various moving objects. In this dissertation, we focus on the `urban vehicle trajectory,' which refers to trajectories of vehicles in urban traffic ne…
