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Recent Releases of afwizard

afwizard - AFWizard v1.0.0

AFWizard is a Python package to enhance the productivity of ground point filtering workflows in archaeology and beyond. It provides a Jupyter-based environment for "human-in-the-loop" tuned, spatially heterogeneous ground point filterings.

Core features:

  • Working with Lidar datasets directly in Jupyter notebooks
    • Loading/Storing of LAS/LAZ files
    • Visualization using hillshade models and slope maps
    • Applying of ground point filtering algorithms
    • Cropping with a map-based user interface
  • Accessibility of existing filtering algorithms under a unified data model:
    • PDAL: The Point Data Abstraction Library is an open source library for point cloud processing.
    • OPALS is a proprietary library for processing Lidar data. It can be tested freely for datasets <1M points.
    • LASTools has a proprietary tool called lasground_new that can be used for ground point filtering.
  • Access to predefined filter pipeline settings
    • Crowd-sourced library of filter pipelines at https://github.com/ssciwr/afwizard-library/
    • Filter definitions can be shared with colleagues as files
  • Spatially heterogeneous application of filter pipelines
    • Assignment of filter pipeline settings to spatial subregions in map-based user interface
    • Command Line Interface for large scale application of filter pipelines

- Python
Published by dokempf almost 3 years ago

afwizard - AFwizard v.1.0.0b9

AFWizard is a Python package to enhance the productivity of ground point filtering workflows in archaeology and beyond. It provides a Jupyter-based environment for "human-in-the-loop" tuned, spatially heterogeneous ground point filterings.

Core features:

  • Working with Lidar datasets directly in Jupyter notebooks
    • Loading/Storing of LAS/LAZ files
    • Visualization using hillshade models and slope maps
    • Applying of ground point filtering algorithms
    • Cropping with a map-based user interface
  • Accessibility of existing filtering algorithms under a unified data model:
    • PDAL: The Point Data Abstraction Library is an open source library for point cloud processing.
    • OPALS is a proprietary library for processing Lidar data. It can be tested freely for datasets <1M points.
    • LASTools has a proprietary tool called lasground_new that can be used for ground point filtering.
  • Access to predefined filter pipeline settings
    • Crowd-sourced library of filter pipelines at https://github.com/ssciwr/afwizard-library/
    • Filter definitions can be shared with colleagues as files
  • Spatially heterogeneous application of filter pipelines
    • Assignment of filter pipeline settings to spatial subregions in map-based user interface
    • Command Line Interface for large scale application of filter pipelines

This is a beta release of afwizard. You are welcome to test the library and report any issues you might find.

- Python
Published by dokempf over 3 years ago

afwizard - AFwizard v.1.0.0b7

AFWizard is a Python package to enhance the productivity of ground point filtering workflows in archaeology and beyond. It provides a Jupyter-based environment for "human-in-the-loop" tuned, spatially heterogeneous ground point filterings.

Core features:

  • Working with Lidar datasets directly in Jupyter notebooks
    • Loading/Storing of LAS/LAZ files
    • Visualization using hillshade models and slope maps
    • Applying of ground point filtering algorithms
    • Cropping with a map-based user interface
  • Accessibility of existing filtering algorithms under a unified data model:
    • PDAL: The Point Data Abstraction Library is an open source library for point cloud processing.
    • OPALS is a proprietary library for processing Lidar data. It can be tested freely for datasets <1M points.
    • LASTools has a proprietary tool called lasground_new that can be used for ground point filtering.
  • Access to predefined filter pipeline settings
    • Crowd-sourced library of filter pipelines at https://github.com/ssciwr/afwizard-library/
    • Filter definitions can be shared with colleagues as files
  • Spatially heterogeneous application of filter pipelines
    • Assignment of filter pipeline settings to spatial subregions in map-based user interface
    • Command Line Interface for large scale application of filter pipelines

This is a beta release of afwizard. You are welcome to test the library and report any issues you might find.

- Python
Published by dokempf over 3 years ago

afwizard - AFwizard v.1.0.0b6

AFWizard is a Python package to enhance the productivity of ground point filtering workflows in archaeology and beyond. It provides a Jupyter-based environment for "human-in-the-loop" tuned, spatially heterogeneous ground point filterings.

Core features:

  • Working with Lidar datasets directly in Jupyter notebooks
    • Loading/Storing of LAS/LAZ files
    • Visualization using hillshade models and slope maps
    • Applying of ground point filtering algorithms
    • Cropping with a map-based user interface
  • Accessibility of existing filtering algorithms under a unified data model:
    • PDAL: The Point Data Abstraction Library is an open source library for point cloud processing.
    • OPALS is a proprietary library for processing Lidar data. It can be tested freely for datasets <1M points.
    • LASTools has a proprietary tool called lasground_new that can be used for ground point filtering.
  • Access to predefined filter pipeline settings
    • Crowd-sourced library of filter pipelines at https://github.com/ssciwr/afwizard-library/
    • Filter definitions can be shared with colleagues as files
  • Spatially heterogeneous application of filter pipelines
    • Assignment of filter pipeline settings to spatial subregions in map-based user interface
    • Command Line Interface for large scale application of filter pipelines

This is a beta release of afwizard. You are welcome to test the library and report any issues you might find.

- Python
Published by dokempf over 3 years ago

afwizard - AFwizard v.1.0.0b5

AFWizard is a Python package to enhance the productivity of ground point filtering workflows in archaeology and beyond. It provides a Jupyter-based environment for "human-in-the-loop" tuned, spatially heterogeneous ground point filterings.

Core features:

  • Working with Lidar datasets directly in Jupyter notebooks
    • Loading/Storing of LAS/LAZ files
    • Visualization using hillshade models and slope maps
    • Applying of ground point filtering algorithms
    • Cropping with a map-based user interface
  • Accessibility of existing filtering algorithms under a unified data model:
    • PDAL: The Point Data Abstraction Library is an open source library for point cloud processing.
    • OPALS is a proprietary library for processing Lidar data. It can be tested freely for datasets <1M points.
    • LASTools has a proprietary tool called lasground_new that can be used for ground point filtering.
  • Access to predefined filter pipeline settings
    • Crowd-sourced library of filter pipelines at https://github.com/ssciwr/afwizard-library/
    • Filter definitions can be shared with colleagues as files
  • Spatially heterogeneous application of filter pipelines
    • Assignment of filter pipeline settings to spatial subregions in map-based user interface
    • Command Line Interface for large scale application of filter pipelines

This is the third beta release of afwizard. You are welcome to test the library and report any issues you might find.

- Python
Published by dokempf over 3 years ago

afwizard - AFWizard v.1.0.0b2

AFWizard is a Python package to enhance the productivity of ground point filtering workflows in archaeology and beyond. It provides a Jupyter-based environment for "human-in-the-loop" tuned, spatially heterogeneous ground point filterings.

Core features:

  • Working with Lidar datasets directly in Jupyter notebooks
    • Loading/Storing of LAS/LAZ files
    • Visualization using hillshade models and slope maps
    • Applying of ground point filtering algorithms
    • Cropping with a map-based user interface
  • Accessibility of existing filtering algorithms under a unified data model:
    • PDAL: The Point Data Abstraction Library is an open source library for point cloud processing.
    • OPALS is a proprietary library for processing Lidar data. It can be tested freely for datasets <1M points.
    • LASTools has a proprietary tool called lasground_new that can be used for ground point filtering.
  • Access to predefined filter pipeline settings
    • Crowd-sourced library of filter pipelines at https://github.com/ssciwr/afwizard-library/
    • Filter definitions can be shared with colleagues as files
  • Spatially heterogeneous application of filter pipelines
    • Assignment of filter pipeline settings to spatial subregions in map-based user interface
    • Command Line Interface for large scale application of filter pipelines

This is the third beta release of afwizard. You are welcome to test the library and report any issues you might find.

- Python
Published by dokempf almost 4 years ago

afwizard - adaptivefiltering v1.0.0b1

adaptivefiltering is a Python package to enhance the productivity of ground point filtering workflows in archaeology and beyond. It provides a Jupyter-based environment for "human-in-the-loop" tuned, spatially heterogeneous ground point filterings.

Core features:

  • Working with Lidar datasets directly in Jupyter notebooks
    • Loading/Storing of LAS/LAZ files
    • Visualization using hillshade models and slope maps
    • Applying of ground point filtering algorithms
    • Cropping with a map-based user interface
  • Accessibility of existing filtering algorithms under a unified data model:
    • PDAL: The Point Data Abstraction Library is an open source library for point cloud processing.
    • OPALS is a proprietary library for processing Lidar data. It can be tested freely for datasets <1M points.
    • LASTools has a proprietary tool called lasground_new that can be used for ground point filtering.
  • Access to predefined filter pipeline settings
    • Crowd-sourced library of filter pipelines at https://github.com/ssciwr/adaptivefiltering-library/
    • Filter definitions can be shared with colleagues as files
  • Spatially heterogeneous application of filter pipelines
    • Assignment of filter pipeline settings to spatial subregions in map-based user interface
    • Command Line Interface for large scale application of filter pipelines

This is the second beta release of adaptivefiltering. You are welcome to test the library and report any issues you might find.

- Python
Published by dokempf almost 4 years ago

afwizard - adaptivefiltering v1.0.0b0

adaptivefiltering is a Python package to enhance the productivity of ground point filtering workflows in archaeology and beyond. It provides a Jupyter-based environment for "human-in-the-loop" tuned, spatially heterogeneous ground point filterings.

Core features:

  • Working with Lidar datasets directly in Jupyter notebooks
    • Loading/Storing of LAS/LAZ files
    • Visualization using hillshade models and slope maps
    • Applying of ground point filtering algorithms
    • Cropping with a map-based user interface
  • Accessibility of existing filtering algorithms under a unified data model:
    • PDAL: The Point Data Abstraction Library is an open source library for point cloud processing.
    • OPALS is a proprietary library for processing Lidar data. It can be tested freely for datasets <1M points.
    • LASTools has a proprietary tool called lasground_new that can be used for ground point filtering.
  • Access to predefined filter pipeline settings
    • Crowd-sourced library of filter pipelines at https://github.com/ssciwr/adaptivefiltering-library/
    • Filter definitions can be shared with colleagues as files
  • Spatially heterogeneous application of filter pipelines
    • Assignment of filter pipeline settings to spatial subregions in map-based user interface
    • Command Line Interface for large scale application of filter pipelines

This is the first beta release of adaptivefiltering. You are welcome to test the library and report any issues you might find.

- Python
Published by dokempf almost 4 years ago