The Efficacy of Automated Remote Sensing Analysis for Archaeology in the American Southeast

Remote-sensing survey has great potential for studying coastal landscapes of the American Southeast. The coasts of Georgia and South Carolina, for example, remain heavily forested and are woven with low-lying marshlands inter-braided with bayous. Thus, accessing this landscape has been traditionally difficult to systematically investigate. The use of LiDAR data generated from aerial platforms, combined with computational methods for isolating features associated with past cultural activity, has great promise to expand our knowledge of this region. Importantly, given that this form of investigation is non-invasive, a study focused on using LiDAR to isolate artificial features will contribute to efforts to preserve the archaeological record. This preservation effort is particularly significant in these low-lying coastal areas that are threatened with destruction from storm-surges and sea-level rise associated with climate change. Finally, with an increase in knowledge of the record, not only will we gain new areas for research, but we will also contribute to local communities who can benefit from increased historical and archaeological tourism. This project seeks to develop a reliable and systematic approach for identifying classes of landforms associated with artificial earthen structures using LiDAR data and statistical algorithms derived from shape analysis of image data. Specifically, I will explore the use of LiDAR and analytic approaches for locating shell deposits that are found in abundance throughout the archaeological record of the coastal regions of the American Southeast. My study seeks to improve existing image analysis algorithms by incorporating template matching and object-based image analysis (OBIA) methods to minimize false positive and negative results. I also integrate the systematic use of land-use maps to further minimize cases in which artificial features are identified that are due to recent human activity, rather than prehistoric depositional events.

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Creators Dylan S. Davis; Robert J. DiNapoli; Matthew C. Sanger;
Deposited October 09, 2019 19:17
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Creators Dylan Davis; Carl Lipo; Matthew Sanger
Deposited September 14, 2019 09:09
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Creators Dylan Davis; Katherine Seeber; Matthew Sanger
Deposited January 25, 2021 13:16
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Creators Dylan Davis
Deposited January 25, 2021 13:40
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Creators Dylan Davis; Matthew Sanger; Carl Lipo
Deposited January 25, 2021 15:24
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Creators Dylan Davis; Matthew Sanger; Carl Lipo
Deposited January 25, 2021 15:36
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Metadata

Title The Efficacy of Automated Remote Sensing Analysis for Archaeology in the American Southeast
Creator Dylan Davis
Publication Date 2018
Geographic Area North America; South Carolina
Deposited at January 25, 2021 14:55

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