Open Open LiDAR Toolbox is a QGIS plug-in for one-step-processing of airborne LiDAR data
from point cloud to LiDAR visualisations.
It is based on peer-reviewed papers published in open access journals
The required input is an unclassified point cloud in LAZ/LAS format and the tool returns outputs needed for interpretative mapping of archaeological features. In addition, several other tools are available for multi-step data processing. The tools are optimised for archaeology, but have a broader application for anyone primarily interested in visual inspection of airborne LiDAR, e.g., topographic mapping.
This is an algorithm pipeline that takes an airborne LiDAR point cloud to produce all derivatives essential for archaeology and anyone interested in visual analysis of LiDAR data or uses it for topographic mapping
The algorithm will classify the airborne LiDAR point cloud. This process – also known as "filtering" or semantic labeling of the point cloud – is optimized for archaeology, but is also useful for other purposes.
This is a pipeline that takes an airborne LiDAR point cloud to produce rasters needed for further processing or used directly in archaeological (or similar) workflows.
This is an algorithm pipeline that takes an airborne LiDAR point cloud to produce a digital feature model (DFM) especially filtered for archaeological purposes
This algorithm calculates a DFM Confidence Map based on the CRAN decision tree. The confidence map is primarily used for the quality assessment of the DFM, but can also be used to determine the optimal resolution for the DFM.
This algorithm calculates a hybrid interpolation of DFM/DEM. It uses IDW (Inverse Distance Weighing) interpolation in areas of low DFM confidence (levels 1-3) and TLI ( Triangulation with Linear Interpolation) interpolation in areas of high DFM confidence (levels 4-6)
This algorithm takes a digital feature model (DFM, which is archaeology-specific DEM) or any DEM to produce the most commonly used archaeological visualisations.
All of the tools are explained in detail in these open access publications:
Štular, Benjamin, Stefan Eichert, and Edisa Lozić. 2021. "Airborne LiDAR Point Cloud Processing for Archaeology. Pipeline and QGIS Toolbox" Remote Sensing 13, no. 16: 3225. https://doi.org/10.3390/rs13163225
Lozić, Edisa, and Benjamin Štular. 2021. "Documentation of Archaeology-Specific Workflow for Airborne LiDAR Data Processing" Geosciences 11, no. 1: 26. https://doi.org/10.3390/geosciences11010026
Štular, Benjamin, Edisa Lozić, and Stefan Eichert. 2021. "Airborne LiDAR-Derived Digital Elevation Model for Archaeology" Remote Sensing 13, no. 9: 1855. https://doi.org/10.3390/rs13091855
Štular, Benjamin, and Edisa Lozić. 2020. "Comparison of Filters for Archaeology-Specific Ground Extraction from Airborne LiDAR Point Clouds" Remote Sensing 12, no. 18: 3025. https://doi.org/10.3390/rs12183025https://doi.org/10.3390/rs12183025
Lozić, Edisa. 2021. "Application of Airborne LiDAR Data to the Archaeology of Agrarian Land Use: The Case Study of the Early Medieval Microregion of Bled (Slovenia)" Remote Sensing 13, no. 16: 3228. https://doi.org/10.3390/rs13163225