Automatic Detection of Pit Structures in Airborne Laser Scanning Data

Abstract

This paper describes a new method for the automatic detection of pit structures in airborne laser scanning data collected with at least five emitted pulses per square metre. Oppland County, Norway, has a large number of ancient iron production sites and hunting systems. Today, these are manifested as pits in the ground. The iron production sites were used 1400–700 yr ago, and consist of charcoal burning pits, often located in groups of four around a central oven, which also occasionally can be located as a small mound. The hunting systems were used 2000–500 yr ago, and consisted of concealed pitfall traps and wooden fences, located on deer trekking routes. The fences are gone, but the pits remain. Many of the iron production sites and hunting systems are located in forested areas. The current, traditional mapping of these is relatively poor, with many individual pits missing, and poor positional accuracy of the ones that are mapped. This means that overlays of cultural heritage sites cannot be trusted when planning new highways, residential areas, cabins, etc. Experience suggests that automatic detection is clearly superior to visual inspection of the lidar data in finding small pits or pits that are not easily visible. However, visual inspection is better at finding additional information as human interpretation based on archaeological knowledge can be applied. Combining these two methods secures a very good basis for subsequent field survey. In conclusion, we demonstrate that automatic detection of hunting systems and iron extraction sites in lidar data is a valuable tool in combination with visual inspection of the lidar data, prior to field survey. Provided that the point density of the lidar data is high enough, the field survey can be accomplished ten times faster compared with the traditional approach without lidar data. Copyright © 2012 John Wiley & Sons, Ltd.

Automatic detection of mound structures in airborne laser scanning data

“…Building on our previous work (Trier et al, 2009;Trier and Pilø, 2012), we propose a processing chain for the automatic detection of mound structures in ALS data (Fig. 5).…”

“…5). This method is a modification of a The 19 grave mounds at Omsland Søndre were previously unknown, and were discovered by our semi-automatic procedure, see Section 4.1. the method we proposed for the detection of pit structures in ALS data (Trier and Pilø, 2012).…”

“…We have recently developed a method for the semi-automatic detection of hunting systems and iron extraction sites from ALS data (Trier and Pilø, 2012). This method is now in use as part of the standard procedure for archaeological mapping in Oppland County, Norway.…”

“…In the latter, pixels are merged into segments (also called objects), which is followed by classification of the segments (or objects). Trier and Pilø (2012) used template matching instead of segmentation to identify candidate objects which were then classified. The difference between segmentation and template matching is that segmentation is a subdivision of the image into non-overlapping objects, whereas template matching may create overlapping objects.…”

Detecting Neolithic Burial Mounds from LiDAR-Derived Elevation Data Using a Multi-Scale Approach and Machine Learning Techniques

“…Object-based image analysis [5] and template matching [6] applied to DTMs or VTs has been used to automatically detect archaeological structures. While effective, this method requires defining a prototypical structure (for template matching) or characteristic spatial attributes (size, shape, orientation, specific VT value, etc.)…”