Mapping and feature classification of low altitude orthomosaics using geospatial image analysis in a planned colonial town in highland Peru

Summary

Large archaeological settlements with complex architecture have been always difficult to map. The introduction of unmanned aerial vehicles to fly over sites has helped reduce the time and increase precision of archaeological mapping; nevertheless post-processing time is still a workflow bottleneck. We present a geospatial imagery-based methodology for identifying and mapping surficially-visible structures and environmental features at a late pre-Hispanic and colonial settlement with extensive and complex architectural features in the highland Andes of Peru. Satellite imagery and aerial photography has been used before in archaeology to identify archaeological sites at a macro scale. Now, the use of high-resolution panchromatic photography analysis allows the identification of individual features in detail. Using feature extraction principles over a high resolution ortho-mosaic image, we identify architectural and natural features at the site. The results enable walls, complete structures and collapsed buildings to be readily identified and mapped as vector themes while also revealing swamp and high-density vegetation areas and wall foundations not observable on the ground in the field. The application of this feature extraction methodology in other archaeological context is also discussed.

SAA 2015 abstracts made available in tDAR courtesy of the Society for American Archaeology and Center for Digital Antiquity Collaborative Program to improve digital data in archaeology. If you are the author of this presentation you may upload your paper, poster, presentation, or associated data (up to 3 files/30MB) for free. Please visit http://www.tdar.org/SAA2015 for instructions and more information.

This Resource is Part of the Following Collections

• Society for American Archaeology 80th Annual Meeting, San Francisco, CA (2015)   •
• Ancient Architecture and Spatial Technology: A Global Perspective

Keywords

Spatial Coverage