Spatiotemporal Datasets

Spatiotemporal datasets, encompassing spatially referenced time series data, are crucial for understanding dynamic phenomena across diverse fields. Current research focuses on developing accurate and efficient prediction models, often employing graph neural networks, transformers, and diffusion models to capture complex spatial and temporal dependencies, while also addressing challenges like data sparsity and uncertainty quantification. These advancements are improving the analysis of large-scale datasets in areas such as urban computing, environmental monitoring, and climate science, leading to more robust and reliable predictions. Furthermore, significant effort is dedicated to enhancing model efficiency and addressing privacy concerns related to the sensitive nature of spatiotemporal data.

Papers

October 14, 2024

Echo State Networks for Spatio-Temporal Area-Level Data
Zhenhua Wang, Scott H. Holan, Christopher K. Wikle
Forecasting Performance Temporal Data Temporal Dynamic Echo State Network Spatiotemporal Datasets State Occupancy Matching

October 9, 2024

Gridded Transformer Neural Processes for Large Unstructured Spatio-Temporal Data
Matthew Ashman, Cristiana Diaconu, Eric Langezaal, Adrian Weller, Richard E. Turner
Neural Process Temporal Data Spatiotemporal Datasets Unstructured Information Large Scale Spatio Temporal Transformer Neural Forecasting Pipeline

September 13, 2024

SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks
Dingyi Zhuang, Yuheng Bu, Guang Wang, Shenhao Wang, Jinhua Zhao
Graph Neural Network Uncertainty Calibration Spatiotemporal Prediction Spatiotemporal Datasets Spatiotemporal Graph Neural Network Spatiotemporal Deep Learning

September 10, 2024

EasyST: A Simple Framework for Spatio-Temporal Prediction
Jiabin Tang, Wei Wei, Lianghao Xia, Chao Huang
Spatio Temporal Information Bottleneck Spatio Temporal Prediction Spatiotemporal Datasets

June 27, 2024

Advection Augmented Convolutional Neural Networks
Niloufar Zakariaei, Siddharth Rout, Eldad Haber, Moshe Eliasof
Long Range Lagrangian System Spatiotemporal Datasets Spatiotemporal Sequence

June 12, 2024

Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks
Wenying Duan, Tianxiang Fang, Hong Rao, Xiaoxi He
Graph Drawing Temporal Graph Neural Network Spectral Graph Pre Training Task Spatiotemporal Datasets Robust Ticket

June 4, 2024

ST-DPGAN: A Privacy-preserving Framework for Spatiotemporal Data Generation
Wei Shao, Rongyi Zhu, Cai Yang, Chandra Thapa, Muhammad Ejaz Ahmed, Seyit Camtepe, Rui Zhang, DuYong Kim, Hamid Menouar, Flora D. Salim
New Framework SpatioTemporal Data Spatiotemporal Datasets

June 1, 2024

Modeling Randomly Observed Spatiotemporal Dynamical Systems
Valerii Iakovlev, Harri Lähdesmäki
Dynamic Model Spatiotemporal Dynamical System Spatiotemporal Datasets Neural Point Process

April 19, 2024

On-Demand Earth System Data Cubes
David Montero, César Aybar, Chaonan Ji, Guido Kraemer, Maximilian Söchting, Khalil Teber, Miguel D. Mahecha
Spatio Temporal Earth System Spatiotemporal Datasets Image Storage

March 22, 2024

An Open-World, Diverse, Cross-Spatial-Temporal Benchmark for Dynamic Wild Person Re-Identification
Lei Zhang, Xiaowei Fu, Fuxiang Huang, Yi Yang, Xinbo Gao
Person Re Identification Open World Diverse Set Spatiotemporal Datasets Temporal Benchmark Domain Diversity Domain Expansion

March 19, 2024

FairSTG: Countering performance heterogeneity via collaborative sample-level optimization
Gengyu Lin, Zhengyang Zhou, Qihe Huang, Kuo Yang, Shifen Cheng, Yang Wang
Resource Allocation Temporal Graph Learning Spatiotemporal Datasets Spatiotemporal Learning Collaborative Optimization

March 12, 2024

Scalable Spatiotemporal Prediction with Bayesian Neural Fields
Feras Saad, Jacob Burnim, Colin Carroll, Brian Patton, Urs Köster, Rif A. Saurous, Matthew Hoffman
Spatio tempOral Spatiotemporal Prediction Spatiotemporal Datasets Bayes Net Bayesian Neural

December 1, 2023

Spatiotemporal Transformer for Imputing Sparse Data: A Deep Learning Approach
Kehui Yao, Jingyi Huang, Jun Zhu
Deep Learning Approach Sparse Data Spatiotemporal Transformer Soil Moisture Spatiotemporal Datasets Spatiotemporal Imputation

September 5, 2023

sasdim: self-adaptive noise scaling diffusion model for spatial time series imputation
Shunyang Zhang, Senzhang Wang, Xianzhen Tan, Ruochen Liu, Jian Zhang, Jianxin Wang
Diffusion Model Temporal Dependency Temporal Convolution Spatiotemporal Datasets

August 25, 2023

Linking the Dynamic PicoProbe Analytical Electron-Optical Beam Line / Microscope to Supercomputers
Alexander Brace, Rafael Vescovi, Ryan Chard, Nickolaus D. Saint, Arvind Ramanathan, Nestor J. Zaluzec, Ian Foster
Data Stream Scientific Workflow Spatiotemporal Datasets Smartphone Microscope Cray XC40 Supercomputer

July 31, 2023

A Trajectory K-Anonymity Model Based on Point Density and Partition
Wanshu Yu, Haonan Shi, Hongyun Xu
Trajectory Datasets Magic Cube Partition Spatiotemporal Datasets Privacy Preserving Trajectory

July 9, 2023

Semi Supervised Meta Learning for Spatiotemporal Learning
Faraz Waseem, Pratyush Muthukumar
Supervised Autoencoder Spatiotemporal Datasets Spatiotemporal Learning Efficient Meta Semi Supervised Meta

June 8, 2023

Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean
Spyros Kondylatos, Ioannis Prapas, Gustau Camps-Valls, Ioannis Papoutsis
Large Scale Dataset Diverse Datasets Spatiotemporal Datasets Wildfire Modeling Wildfire Danger

April 27, 2023

LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction
Jiawei Jiang, Chengkai Han, Wenjun Jiang, Wayne Xin Zhao, Jingyuan Wang
High Efficiency Urban Environment Spatio Temporal Prediction Unified Library Spatiotemporal Datasets Urban Spatial Temporal Prediction

September 28, 2022

Semantic Segmentation of Vegetation in Remote Sensing Imagery Using Deep Learning
Alexandru Munteanu, Marian Neagul
Deep Learning Convolutional Neural Network Semantic Segmentation Remote Sensing Remote Sensing Image Satellite Imagery Tree Specie Spatiotemporal Datasets

Spatiotemporal Datasets

Papers

Echo State Networks for Spatio-Temporal Area-Level Data

Gridded Transformer Neural Processes for Large Unstructured Spatio-Temporal Data

SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks

EasyST: A Simple Framework for Spatio-Temporal Prediction

Advection Augmented Convolutional Neural Networks

Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks

ST-DPGAN: A Privacy-preserving Framework for Spatiotemporal Data Generation

Modeling Randomly Observed Spatiotemporal Dynamical Systems

On-Demand Earth System Data Cubes

An Open-World, Diverse, Cross-Spatial-Temporal Benchmark for Dynamic Wild Person Re-Identification

FairSTG: Countering performance heterogeneity via collaborative sample-level optimization

Scalable Spatiotemporal Prediction with Bayesian Neural Fields

Spatiotemporal Transformer for Imputing Sparse Data: A Deep Learning Approach

sasdim: self-adaptive noise scaling diffusion model for spatial time series imputation

Linking the Dynamic PicoProbe Analytical Electron-Optical Beam Line / Microscope to Supercomputers

A Trajectory K-Anonymity Model Based on Point Density and Partition

Semi Supervised Meta Learning for Spatiotemporal Learning

Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean

LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction

Semantic Segmentation of Vegetation in Remote Sensing Imagery Using Deep Learning