Adaptive Ground Point Filtering for Aerial Archaeology

Airborne Laser Scanning is an interesting tool in many applications including archaelogical prospection. The raw point cloud data is filtered using classification algorithms to obtain archaelogically relevant digital terrain models. To achieve the necessary accuracy, parameters of the ground point filtering algorithms need to be fine-tuned to the environmental conditions (vegetation type, topography etc.). In this project, we develop a Python library that implements a human-in-the-loop optimization process for the configuration of existing ground point filtering algorithms.


The development process can be followed here:

This project is funded through the 2021 SSC Open Call with a total of 4 months of developer time.

Applicant: PD Dr. Maria Shinoto, Institut für Ur- und Frühgeschichte