Tag Archives: semi-automatic detection

LiDAR based semi-automatic pattern recognition within an archaeological landscape

A PhD-project in association with Dr. Armin Volkmann (Junior Research Group Digital Humanities and Digital Cultural Heritage at the Interdisciplinary Center for Scientific Computing and Cluster of Excellence Asia & Europe in Global Context), Prof. Dr. Alexander Zipf and Jun.-Prof. Bernhard Höfle (Geoinformatics), Prof. Dr. Diamantis Panagiotopoulos (Institute of Archaeology), and funded by the HGS MathComp Graduate School at Heidelberg University. In further collaboration with Bayerisches Landesamt für Vermessung (BLV) und Denkmalpflege (BLD), and Karlsruhe Institut für Technologie (KIT).

The project 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 3D, 2½D, and 2D LiDAR derived data.

The utilization of LiDAR provides several novel approaches for locating and monitoring cultural heritage, especially in areas of logistical complications, e.g. forest, rough terrain, and remote areas (cf. figure 1 & 2). In order to cope with the huge amount of generated 3D LiDAR point clouds, systematic and semi-automated procedures needs to be defined to control and handle these accumulated amounts of otherwise unrestrained information.

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

Further, a research data gateway will be created within a WebGIS for extended parameterization of 2D and 3D LiDAR data with added databases of archaeological site information.

Figure 1.  Multiple layers on reality. Combining laser scanning for optimal scaling possibilities together with other sources, often reveal new features yet unknown. Figure 1 displays airborne and terrestrial laser scanning combined with street map at the Königstuhl, Heidelberg. New features detected; a house, two cellar structures, and several pathway and terrace systems dating back to the 16th and 17th century. Source: ALS Landesamt für Geoinformation und Landentwicklung Baden-Württemberg. TLS: Höfle, Pfeiffer and Raun. Map: OpenStreetMap.

Figure 1.
Multiple layers on reality. Combining laser scanning for optimal scaling possibilities together with other sources, often reveal new features yet unknown. Figure 1 displays airborne and terrestrial laser scanning combined with street map at the Königstuhl, Heidelberg. New features detected; a house, two cellar structures, and several pathway and terrace systems dating back to the 16th and 17th century. Source: ALS Landesamt für Geoinformation und Landentwicklung Baden-Württemberg. TLS: Höfle, Pfeiffer and Raun. Map: OpenStreetMap.

Figure 2. Revealing landscape. Underneath the underwood on Reussenberg near Karsbach, several new details arise in the interpolated landscape around the 13th century Reussenburg ruin. Source: ALS and historical maps: Bayerisches Landesamt für Vermessung.

Figure 2.
Revealing landscape. Underneath the underwood on Reussenberg near Karsbach, several new details arise in the interpolated landscape around the 13th century Reussenburg ruin. Source: ALS and historical maps: Bayerisches Landesamt für Vermessung.