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@HPSCIL

High-performance Spatial Computational Intelligence Lab @ China University of Geosciences (Wuhan)

Popular repositories

  1. cuSTARFM Public

    cuSTARFM is a GPU-enabled Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM)

    Cuda 56 34

  2. The PLUS model integrates a rule mining framework based on Land Expansion Analysis Strategy (LEAS) and a CA model based on multi-type Random Patch Seeds (CARS), which was used to understand the dri…

    47 19

  3. cuESTARFM is a GPU-enabled enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM)

    Cuda 36 21

  4. The Mixed-Cell Cellullar Automata (MCCA) provides a new approach to enable more dynamic mixed landuse modeling to move away from the analysis of static patterns. One of the biggest advantages of mi…

    25 8

  5. cuSTNLFFM is a GPU-enabled Spatial and Temporal Non-Local Filter-based Fusion Model (STNLFFM)

    Cuda 18 9

  6. pRPL Public

    parallel Raster Processing Library (pRPL) is a MPI-enabled C++ programming library that provides easy-to-use interfaces to parallelize raster/image processing algorithms

    C++ 17 12

Repositories

  • Patch-generating_Land_Use_Simulation_Model Public

    The PLUS model integrates a rule mining framework based on Land Expansion Analysis Strategy (LEAS) and a CA model based on multi-type Random Patch Seeds (CARS), which was used to understand the drivers of land expansion and project landscape dynamics.

    47 19 0 0 Updated May 6, 2022
  • Open-Space-Cellular_Automata Public

    A spatio-temporal approach based on Cellular Automata (CA) for simulating the spatial dynamics of open spaces (include urban green spaces, parks, squares, trails, courtyards, and other natural spaces), by considering a set of spatial data that represents the infrastructural and socio-economic factors, namely the OS-CA (Open Space Cellular Autom…

    12 7 0 0 Updated Mar 28, 2022
  • Mixed_Cell_Cellullar_Automata Public

    The Mixed-Cell Cellullar Automata (MCCA) provides a new approach to enable more dynamic mixed landuse modeling to move away from the analysis of static patterns. One of the biggest advantages of mixed-cell CA models is the capability of simulating the quantitative and continuous changes of multiple landuse components inside cells.

    25 8 0 0 Updated Mar 28, 2022
  • cuFSDAF Public

    cuFSDAF is an enhanced FSDAF algorithm parallelized using GPUs. In cuFSDAF, the TPS interpolator is replaced by a modified Inverse Distance Weighted (IDW) interpolator. Besides, computationally intensive procedures are parallelized using the Compute Unified Device Architecture (CUDA), a parallel computing framework for GPUs. Moreover, an adaptiv…

    C++ 16 GPL-3.0 8 3 0 Updated Mar 13, 2022
  • cuSTSG Public

    cuSTSG is a GPU-enabled spatial-temporal Savitzky-Golay (STSG) program based on the Compute Unified Device Architecture (CUDA). Firstly, the cosine similarities between the annual NDVI time series are used to identify and exclude the NDVI values with inaccurate quality flags from the NDVI seasonal growth trajectory. Secondly, the computational p…

    Cuda 3 4 1 0 Updated Sep 25, 2021
  • 0 4 0 0 Updated Jun 1, 2021
  • cuSTARFM Public

    cuSTARFM is a GPU-enabled Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM)

    Cuda 56 GPL-3.0 34 4 0 Updated Jun 1, 2021
  • cuESTARFM Public

    cuESTARFM is a GPU-enabled enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM)

    Cuda 36 GPL-3.0 21 6 0 Updated Jun 1, 2021
  • cuSTNLFFM Public

    cuSTNLFFM is a GPU-enabled Spatial and Temporal Non-Local Filter-based Fusion Model (STNLFFM)

    Cuda 18 GPL-3.0 9 3 0 Updated Jun 1, 2021
  • SSDGL Public

    SSDGL: A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification (TCYB2021) https://ieeexplore.ieee.org/document/9440852

    Python 0 GPL-3.0 14 0 0 Updated May 31, 2021

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