spatiotemporal-data-analysis

There are 27 repositories under spatiotemporal-data-analysis topic.

  • tsl

    TorchSpatiotemporal/tsl

    tsl: a PyTorch library for processing spatiotemporal data.

    Language:Python25793824
  • PAMI

    UdayLab/PAMI

    PAMI is a Python library containing 100+ algorithms to discover useful patterns in various databases across multiple computing platforms. (Active)

    Language:Jupyter Notebook245530195
  • grin

    Graph-Machine-Learning-Group/grin

    Official repository for the paper "Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks" (ICLR 2022)

    Language:Python14141029

  • AILAB-CEFET-RJ/stconvs2s

    Code for the paper "STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for Weather Forecasting" (Neurocomputing, Elsevier)

    Language:Jupyter Notebook638523

  • google/bayesnf

    Bayesian Neural Field models for prediction in large-scale spatiotemporal datasets

    Language:Python486193

  • Graph-Machine-Learning-Group/spin

    Official repository for the paper "Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations" (NeurIPS 2022)

    Language:Python424138

  • johannesuhl/shapefile2gif

    Given a shapefile with time-annotated vector objects (e.g., building footprints + construction year), this script will automatically create an animated GIF illustrating the dynamics for a user-specified period of time

    Language:Python34206

  • yanganYNU/AFFGCN

    Attention Feature Fusion base on spatial-temporal Graph Convolutional Network（AFFGCN）

    Language:Python34321
  • neural-flows

    brain-modelling-group/neural-flows

    Estimation, analysis and decomposition of brainwave spatiotemporal dynamics

    Language:MATLAB2741812

  • pientist/soccercpd

    [KDD 2022] Official implementation of "SoccerCPD: Formation and Role Change-Point Detection in Soccer Matches Using Spatiotemporal Tracking Data".

    Language:Jupyter Notebook25300

  • Rose-STL-Lab/AutoSTPP

    Automatic Integration for Neural Spatio-Temporal Point Process models (AI-STPP) is a new paradigm for exact, efficient, non-parametric inference of point process. It is capable of learning complicated underlying intensity functions, like a damped sine wave.

    Language:Jupyter Notebook23102

  • pientist/ballradar

    [KDD 2023] Official implementation of "Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM".

    Language:Jupyter Notebook20503

  • PREP-NexT/locust-climate-DMD

    The official repository for "Unveiling the Role of Climate in Spatially Synchronized Locust Outbreak Risks"

    Language:Python6101

  • papari1123/Research-of-Particulate-Matter-Prediction-Modeling-Based-on-Deep-Learning

    This repository introduces Deep Particulate Matter Network with a Separated Input model based on deep learning by using ConvGRU, which can simultaneously analyze spatiotemporal information to consider the diffusion of particulate matter.

    Language:Jupyter Notebook4112

  • r-a-dobson/dynamicSDM

    An R package for species geographical distribution and abundance modelling at high spatiotemporal resolution

    Language:R4292

  • tabithaks/Capstone-GE-Asset-Tracking

    Columbia University Data Science Master Capstone Project. The goal of this project was to cluster trajectories by shape for later optimization.

    Language:Jupyter Notebook2200

  • EdgarACarneiro/stfX

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

    Language:Python1212

  • most-inesctec/stfX

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

    Language:Python1200

  • sigmafelix/misu-unemployment-us

    Spatiotemporal variability in the association between mental illness and substance use mortality and unemployment in the contiguous US

    Language:R1100

  • sumdher/trajectory_segmenter_movmedspd

    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.

    Language:Python1100

  • Ashish-Kondal/Preprocessing_for_AgroHydrological_Modeling

    Preprocessing Scripts for VIC-CropSyst Modeling

    Language:Python0100

  • genema/STE

    [AAAI] A spatiotemporal embedding framework for geographical entities

    0100

  • syedmfuad/agri_grid_data

    Harmonize heterogenous spatiotemporal gridded agriculture-related datasets. Part of a larger ongoing project to monitor land and water use by combining irrigation and gridded data via remote sensing data with machine learning.

    Language:R0100

  • jj11031/Movement-Ecology-Visualizations

    Few graphs/plots and maps as outputs of movement ecology research (GPS telemetry)

    10

  • KJSloan2/EO

    EO is an earth observation (EO) toolkit for land analysis. Focus areas include Landsat 8/9 raster data, 3DEP elevation data and USGS features.

    Language:Python10

  • Loccx78vn/Spatiotemporal

    10

  • sigmafelix/mim-greenspace-pnw

    Spatial disparity in tract-level associations between mental illness mortality and greenspace exposure in the Pacific Northwest

    Language:R10