PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)

tsl: a PyTorch library for processing spatiotemporal data.

Spatiotemporal modeling of spatial transcriptomics

Self-supervised Spatiotemporal Learning via Video Clip Order Prediction

Revealing Occlusions with 4D Neural Fields (CVPR 2022 Oral) - Official Implementation

(Journal of Hydrology 2025) U-RNN High-resolution spatiotemporal nowcasting of urban flooding

Physics-informed deep super-resolution of spatiotemporal data

PDE-VAE: Variational Autoencoder for Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning

A Python Package for Adaptive Spatio-Temporal Exploratory Model (AdaSTEM)

A collection of deep learning models for remote sensing spatiotemporal fusion.

A python library for lossy compression/serialization of trajectory data based on Google's Encoded Polyline Algorithm Format

Storing GIS data with temporal information (events data) in k-dimensional trees for efficient querying, designed to aid clustering algorithms such as DBSCAN.

Code, logs, and final models for SIGSPATIAL SpatialEpi '22: Spatiotemporal Disease Case Prediction using Contrastive Predictive Coding.

Spatial analysis using Deep Spatio Temporal Point Process (DeepSTPP) updated with state-of-the-art mamba state space model, using spatiotemporal Hawkes Process, spatiotemporal self-correcting process, NJ COVID-19 and Japanese earthquake data.

FLUID-LLM: Learning Computational Fluid Dynamics with Spatiotemporal-aware Large Language Models

Twitter Semantic Data Mining for Harvey Hurricane Credible Event Detection

Extracts low speed segments from spatiotemporal trajectories using moving median of speed. Fast and robust. Adaptively determines the parameters from the data, instead of setting objective, arbitrary parameters. Each trajectory in a set of trajectories will have unique subjective parameters.

SpatioTemporal Features eXtractor: a tool for change detection on spatiotemporal phenomena

SpatioTemporal Features eXtractor: a tool for change detection on spatiotemporal phenomena

Spatiotemporal Gaussian process modeling for environmental data: non-stationary PDE prior, deep kernels, multi-fidelity fusion, and A-optimal sampling.非稳态 PDE + 核深度学习 + 多保真 Co-Kriging + 主动采样的物理约束克里金方法，用于复杂时空环境建模与预测