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

Abstract

Airborne LiDAR technology is widely used in archaeology and over the past decade has emerged as an accurate tool to describe anthropomorphic landforms. Archaeological features are traditionally emphasised on a LiDAR-derived Digital Terrain Model (DTM) using multiple Visualisation Techniques (VTs), and occasionally aided by automated feature detection or classification techniques. Such an approach offers limited results when applied to heterogeneous structures (different sizes, morphologies), which is often the case for archaeological remains that have been altered throughout the ages. This study proposes to overcome these limitations by developing a multi-scale analysis of topographic position combined with supervised machine learning algorithms (Random Forest). Rather than highlighting individual topographic anomalies, the multi-scalar approach allows archaeological features to be examined not only as individual objects, but within their broader spatial context. This innovative and straightforward method provides two levels of results: a composite image of topographic surface structure and a probability map of the presence of archaeological structures. The method was developed to detect and characterise megalithic funeral structures in the region of Carnac, the Bay of Quiberon, and the Gulf of Morbihan (France), which is currently considered for inclusion on the UNESCO World Heritage List. As a result, known archaeological sites have successfully been geo-referenced with a greater accuracy than before (even when located under dense vegetation) and a ground-check confirmed the identification of a previously unknown Neolithic burial mound in the commune of Carnac.

Using deep neural networks on airborne laser scanning data: Results from a case study of semi‐automatic mapping of archaeological topography on Arran, Scotland

“…Template matching has been used to map burials from optical satellite data (Trier et al, ), and to identify a range of objects including pitfall traps, charcoal burning platforms, and grave mounds in a digital terrain model (DTM) derived from ALS (Schneider, Takla, Nicolay, Raab, & Raab, ; Trier & Pilø, ; Trier & Pilø, ; Trier, Pilø, & Johansen, ; Trier, Zortea, & Tonning, ). Also based on a DTM is an automatic pit filling method based on an inverted DTM to locate mound structures (Freeland et al, ); a combination of curvature estimates, topographic position index, and circular Hough transform to detect prehistoric barrows (Cerrillo‐Cuena, ); a combination of segmentation and template matching to detect grazing structures (Toumazet et al, ); and local contrast in the DTM at three different scales and a random forest classifier to detect burial mounds (Guyot et al, ). A study to detect rectangular enclosures in panchromatic satellite images (Zingman et al, ) concluded that bespoke methods in some cases perform better than using a pre‐trained deep CNN, but at the cost of much longer development time.…”

Object‐based image analysis: a review of developments and future directions of automated feature detection in landscape archaeology

“…Morphological variables used for classification include: Circularity (Davis et al, ; Freeland et al, ; Witharana et al, ) Rectangularity (Zingman et al, ) Area (Davis et al, ; Magnini et al, ; Witharana et al, ) Length and width (Magnini et al, ; Toumazet, Vautier, Roussel, & Dousteyssier, ) Size (Cerrillo‐Cuenca, ; Davis et al, ; Zingman et al, ) Curvature (Cerrillo‐Cuenca, ) Edge detection (Traviglia & Torsello, ; Witharana et al, ; Zingman et al, ) Elevation (Davis et al, ; Guyot et al, ) …”

“…Implementing OBIA with a greater number of variables (including multiple scales of analysis) and using higher‐resolution datasets improves accuracy for archaeological prospection (e.g. Guyot, Hubert‐Moy, & Lorho, ; Sevara & Pregesbauer, , 142; Witharana, Ouimet, & Johnson, ).…”

“…The most recent archaeological uses of OBIA and machine learning yield highly accurate results (Freeland et al, ; Guyot et al, ; Lasaponara & Masini, ; Wang et al, ). Freeland et al () demonstrate the first use of hydrological depression algorithms for archaeological mound detection.…”

“…In this instance, an inversed DEM was created and processed through an algorithm that looks for topographic depressions, effectively identifying and mapping mound features (also see Davis, Lipo, & Sanger, in press). The work of Guyot et al () is a strong case for the use of automated object extraction methods within archaeology, as their multiscalar algorithm successfully identified over 2000 Neolithic burial mounds, while false positives ( n = 41) and false negatives ( n = 46) were minimal. The lesson from this latest research are simple: landscape level archaeological prospection algorithms must be multiscalar .…”