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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.

Manual Point Cloud Classification and Extraction for Hunter-Gatherer Feature Investigation: A Test Case From Two Low Arctic Paleo-Inuit Sites