Archaeological Ground Point Filtering of Airborne Laser Scan Derived Point-Clouds in a Difficult Mediterranean Environment

Abstract

Digital terrain models (DTM) based on airborne laser scanning (ALS) are an important source for identifying and monitoring archaeological sites and landscapes. However, a DTM is only one of many representations of a given surface. Its accuracy and quality must conform to its purpose and are a result of several considerations and decisions along the processing chain. One of the most important factors of ALS-based DTM generation is ground point filtering, i.e., the classification of the acquired point-cloud into terrain and off-terrain points. Filtering is not straightforward. The resulting DTM is usually a compromise that might show the surface below very dense vegetation while losing detail in other areas. In this paper, we show that in very complex situations (e.g., strongly varying vegetation cover), an optimal compromise is difficult to achieve, and more than one filter with different settings adapted to the varying degree of vegetation cover is necessary. For practical reasons, the results need to be combined into a single DTM. This is demonstrated using the case study of a Mediterranean landscape in Croatia, which consists of open areas (agricultural and grassland), olive plantations, as well as extremely dense and evergreen macchia vegetation. The results are the first step toward an adaptive ground point filtering strategy that might be useful far beyond the field of archaeology.

Comparison of Filters for Archaeology-Specific Ground Extraction from Airborne LiDAR Point Clouds

“…Airborne LiDAR-derived data provide archaeologists with detailed topography, which is a powerful resource for interpretation [10]. The often described process from data acquisition to archaeological interpretation [11][12][13][14][15][16] can be divided for the purposes of this article into raw data processing (project planning, system calibration, data acquisition, georeferencing, flight strip adjustment), point cloud processing (ground point filtering, additional processing), creation of end products (archaeology-specific raster elevation model interpolation and it visualization), and archaeological interpretation (interpretative mapping, ground inspection, deep integrated multi-scale interpretation).…”

“…For archaeology, the most critical part of the workflow is ground point filtering [16], and this is the subject of our article.…”

“…It is worth reiterating that this process involves assumptions and decisions during the entire workflow, but these are often underestimated or even ignored [12,13]. As some of the end products (for example, a simple hill-shaded representation of the DTM) are almost self-explanatory, this may lead to a certain ignorance of the underlying data processing strategies [16].…”

“…First, visual inspection of enhanced raster visualizations remains the key method. The digital surface model (DSM) used often combines a digital terrain model (DTM) with off-terrain archaeological features [16] and modern buildings [10,17,18]. Such a specific DSM has been termed the digital feature model (DFM) [19].…”

“…It is therefore less important whether the filtering of the point cloud takes 4 or 400 s than whether a single feature is incorrectly represented. For this purpose, archaeology-specific workflows have been proposed for both custom [16] and general-purpose data [15], none of which are as yet generally accepted. Two reasons for this are that the proposed workflows are too complex and in parts based on costly software.…”