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](https://pdal.io/): The Point Data Abstraction Library is an open source library for point cloud processing.
* [OPALS](https://opals.geo.tuwien.ac.at/html/stable/index.html) is a proprietary library for processing Lidar data. It can be tested freely for datasets <1M points.
* [LASTools](https://rapidlasso.com/) 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.