For archaeologists, the task of processing large terrestrial laser scanning (TLS)-derived point
cloud data can be difficult, particularly when focusing on acquiring analytical and interpretive outcomes
from the data. Using our TLS lidar data collected in 2013 from two compositionally different, low Arctic
multi-component hunter-gatherer sites (LdFa-1 and LeDx-42), we demonstrate how a manual point cloud
classification approach with open source software can be used to extract natural and archaeological
features from a site’s surface. Through a combination of spectral datasets typical to TLS (i.e., intensity and
RGB values), archaeologists can enhance the visual and analytical representation of archaeological huntergatherer
site surfaces. Our approach classifies low visibility Arctic site point clouds into independent
segments, each representing a different surface material found on the site. With the segmented dataset, we
extract only the surface boulders to create an alternate characterization of the site’s prominent features and
their surroundings. Using surface point clouds from Paleo-Inuit sites allows us to demonstrate the value of
this approach within hunter-gatherer research as our results illustrate an effective use of large TLS datasets
for extracting and improving our analytical capabilities for low relief site features.
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