Author ORCID Identifier
https://orcid.org/0000-0002-5783-3578, https://orcid.org/0000-0003-4391-3590, , https://orcid.org/0000-0002-0553-8809,
Object based image analysis, template matching, automatic feature identification, remote sensing, shell rings, LiDAR, American Southeast
One persistent archaeological challenge is the generation of systematic documentation for the extant archaeological record at the scale of landscapes. Often our information for landscapes is the result of haphazard and patchy surveys that stem from opportunistic and historic efforts. Consequently, overall knowledge of some regions is the product of ad hocsurvey area delineation, degree of accessibility, effective ground visibility, and the fraction of areas that have survived destruction from development. These factors subsequently contribute unknown biases to our understanding of chronology, settlements patterns, interaction, and exchange. Aerial remote sensing offers one potential solution for improving our knowledge of landscapes. With sensors that include LiDAR, remote sensing can identify archaeological features that are otherwise obscured by vegetation. Object-based image analyses (OBIA) of remote sensing data hold particular promise to facilitate regional analyses thorough the automation of archaeological feature recognition. Here, we explore four OBIA algorithms for artificial mound feature detection using LiDAR from Beaufort County, South Carolina: multiresolution segmentation, inverse depression analysis, template matching, and a newly designed algorithm that combines elements of segmentation and template matching. While no single algorithm proved to be consistently superior to the others, a combination of methods is shown to be the most effective for detecting archaeological features.
To be published in the Journal of Archaeological Science
Davis, Dylan S.; Lipo, Carl P.; and Sanger, Matthew, "A comparison of automated object extraction methods for mound and shell-ring identification in coastal South Carolina" (2018). Anthropology Datasets. 4.
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