Pattern recognition and archaeology – Semi-automated detection of ground monuments in airborne laser scan data (LiDAR)

In recent years, LiDAR scanning has become a widely used tool for understanding both the cultural and natural landscape surrounding us. The result of this is, that new 3-dimensional point based LiDAR (Light Detection and Ranging) data is consistently being generated in a more precise and detailed version of raw point cloud form.

Airborne laser scan data includes information about the ground surface as well as the existing vegetation allowing to virtually remove for instance forests to reveal hidden structures. Even ground monuments which are heavily eroded and/or very flat can be detected by this approach. (Interpolated) surface models are often used to visualize the findings.

Available LiDAR scans of the Bavarian State Office for Surveying and Geoinformation for the district of Lower Franconia with its 8531 km2 as our test area accumulates to 2.8 TB of data. An editor familiar with the data can process approximately 30 km2 per day, resulting in 1.2 years for a one-time analysis of Lower Franconia and already ten years for the state Bavaria. Continuous and iterative explorations increase the amount of data to be handled and lead to time frames not feasible for research.

In order to cope with the increasing amount of spatial and radiometric information, a systematic and semi-automated process needs to be defined in order to control and handle these accumulated amounts of otherwise unrestrained information. Thus the collaborate project intends to focus on the development of technologies and methodologies for the creation of an interactive research data gateway.

The utilization of LiDAR data provides a novel approach for locating and monitoring cultural heritage in the landscape, especially in areas of logistical complications, e.g. forest, rough terrain, and remote areas. However, the utilisation of LiDAR presently lacks standardised approaches for proper handling, developing, and processing within the field of cultural heritage management. Further, a majority of stakeholders within the field of archaeology and cultural heritage management encounters various problems regarding macro- and micromanagement when handling and processing LiDAR data, often resolving in quantitative assessment being impractical or impossible. Thus, in order for LiDAR data to become a truly competent method for heritage management, a large scale quantitative approach for handling, developing, and processing needs to be formed and defined. Such is the background, premise and resolve for this project.

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Figure 1: On the left no archaeological ground monuments can be seen in the aerial image of the forest nearby Urphar (at River Main, District Unterfranken in Germany). With the point signature finds of ceramic shards of the late Bronze Age are marked in Geographic Information System (GIS). The sits information is taken from the databases of the Bavarian State Office for the Preservation of Historical Monuments, which is a partner of our project. The right picture shows the LIDAR scan of the so called DGM1 ground surface of the Bavarian State Office for Surveying and Geoinformation in the same area without any disturbing vegetation. It uncovers a group of prehistoric grave hills. These were previously unknown because they are heavily eroded and very flat. The funnel in the center of each burial mounds gives information about illegal grave robberies.

 

Sub-project

The sub-project, LiDAR based semi-automatic pattern recognition within an archaeological landscape, is focused on adapting and creating semi-automatic procedures for handling and processing LiDAR data within cultural heritage monument detection and large scale cultural heritage management.

Particular emphasis is on the implementation of pattern recognition algorithms for semi-automatic detection within 3dimensional vector and 2dimensional raster data.

In doing so, the effort of this project will be focused on pattern recognition algorithms in order to define quantitative methods of handling and processing 3dimensional LiDAR data and subsequent 2dimensional raster by implementing standardised and state of the art systematic and semi-automated approaches for cultural heritage detection and management.

Besides state of the art algorithms for automatic procedures of cultural heritage detection and management, the result of the project will also be focused on key technologies regarding data life cycle in joint collaboration with the Software Methods Group (SWM) and the KIT (Karlsruhe Institute of Technology). The joint collaboration will be focused on data sustainability, and optimizing visualisation procedures for differentiated fields of focus.

 

Acknowledgments

In addition to the Bavarian State Offices for Sites and Monuments (Bayerisches Landesamt für Denkmalpflege) and the Environment (Bayerisches Landesamt für Umwelt), we would particularly like to thank the Bavarian State Office for Surveying and Geoinformation (Bayerisches Landesamt für Vermessung und Geoinformation) for providing access to unpublished data like the DTM1s. Without the cooperation of these authorities this project and associated project, Transformation of Settlement Sites from Late Antiquity to the Early Middle Ages in Lower Franconia, would not have been possible.


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