Abstract

The use of topographic airborne LiDAR data has become an essential part of archaeological prospection, and the need for an archaeology-specific data processing workflow is well known. It is therefore surprising that little attention has been paid to the key element of processing: an archaeology-specific DEM. Accordingly, the aim of this paper is to describe an archaeology-specific DEM in detail, provide a tool for its automatic precision assessment, and determine the appropriate grid resolution. We define an archaeology-specific DEM as a subtype of DEM, which is interpolated from ground points, buildings, and four morphological types of archaeological features. We introduce a confidence map (QGIS plug-in) that assigns a confidence level to each grid cell. This is primarily used to attach a confidence level to each archaeological feature, which is useful for detecting data bias in archaeological interpretation. Confidence mapping is also an effective tool for identifying the optimal grid resolution for specific datasets. Beyond archaeological applications, the confidence map provides clear criteria for segmentation, which is one of the unsolved problems of DEM interpolation. All of these are important steps towards the general methodological maturity of airborne LiDAR in archaeology, which is our ultimate goal.

Highlights

  • The use of topographic airborne LiDAR data has become an essential part of archaeological prospection [1], e.g., [2,3,4]

  • Very little attention has been paid to the specifics of archaeology-specific

  • We first defined a digital feature model (DFM) as a subtype of digital elevation models (DEMs), describing the archaeology-specific gridded surface model of elevations; this is interpolated from airborne LiDAR-derived ground points, buildings (ASPRS classes 2 and 6, respectively), and archaeological features

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Summary

Introduction

The use of topographic airborne LiDAR data ( known as airborne laser scanning or ALS) has become an essential part of archaeological prospection [1], e.g., [2,3,4]. Archaeologists interpret enhanced visualizations of high-resolution digital elevation models (DEMs) interpolated from classified point clouds [5,6,7]. The need for an archaeology-specific data processing workflow is well established [4,17,18,19,20,21], as are the main reasons for it [1]: . The main method is visual inspection of enhanced raster visualization, possibly supported by machine learning tools The results have proven to be very efficient in detecting archaeological features, and have already drastically changed our understanding of archaeological sites, monuments, and landscapes, especially in forested areas, as seen in [8,9,10,11,12,13,14,15,16].

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