Pattern recognition and automatic feature extraction in GIS.
Archaeological applications of geographic information systems and remote sensing technologies are becoming increasingly popular, especially in regard to site prospection and the geospatial analysis of cultural features. Utilizing aerial LiDAR and high-resolution satellite imagery of North Dakota, a training data set was used to define the boundaries and characteristics for certain morphological features of anthropogenic origin, which include mounds, earth lodge depressions, and fortification ditches. From this, a data mining algorithm was developed to adapt machine learning into an automated extraction program. This system was then tested on other data sets aimed at detecting similar, unrecorded features in the landscape, and verified in the field through ground truthing to determine accuracy. Implications for the successful development of this technology will allow archaeological investigators to review topography and locate specific anthropogenic features on the surface that can otherwise be difficult to distinguish in the field due to vegetation cover, terrain, or landowner permissions. Additionally, it could reduce the amount of time ground crews spend in the field and provide the researcher with site leads and an accurate model of feature distribution within a project area.
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Pattern recognition and automatic feature extraction in GIS.. Matthew Radermacher, Stephanie Day, Anne Denton, Jeffrey Clark, Donald Schwert. Presented at The 80th Annual Meeting of the Society for American Archaeology, San Francisco, California. 2015 ( tDAR id: 397756)
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