A comparison of automated object extraction methods for mound and shell-ring identification in coastal South Carolina

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 hoc survey 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.

Files

Metadata

Work Title A comparison of automated object extraction methods for mound and shell-ring identification in coastal South Carolina
Access
Open Access
Creators
  1. Dylan Davis
  2. Matthew Sanger
  3. Carl Lipo
Keyword
  1. object-based image analysis
  2. template matching
  3. automatic feature identification
  4. remote sensing
  5. shell rings
  6. LiDAR
  7. American Southeast
License Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Work Type Article
Publisher
  1. Elsevier
Publication Date 2019
Publisher Identifier (DOI)
  1. 10.1016
Geographic Area
  1. North America
  2. South Carolina
Related URLs
Deposited January 25, 2021

Versions

Analytics

Collections

Work History

Version 1
published

  • Created
  • Added Creator Dylan Davis
  • Added Creator Matthew Sanger
  • Added Creator Carl Lipo
  • Added Davis_etal_JASREP_2019_AV.pdf
  • Updated License Show Changes
    License
    • https://creativecommons.org/licenses/by-nc-nd/4.0/
  • Published

Version 2
published

  • Created
  • Updated Publisher Identifier (DOI), Related URLs Show Changes
    Publisher Identifier (DOI)
    • 10.1016/j.jasrep.2018.10.035
    • 10.1016
    Related URLs
    • https://doi.org/10.1016/j.jasrep.2018.10.035
  • Published