CAA2016 has ended
Back To Schedule
Thursday, March 31 • 11:20 - 11:45
S20-07 Experiments in the automatic detection of archaeological features in remotely sensed data from Great Plains USA villages

Sign up or log in to save this to your schedule, view media, leave feedback and see who's attending!

Experiments in the Automatic Detection of Archaeological Features in Remotely Sensed Data from Great Plains USA Villages

Kenneth L Kvamme

Numerous prehistoric villages associated with native farming tribes of the Great Plains, USA, have been investigated through ground-based geophysics and aerial remote sensing, including lidar. These villages vary from 1-20 ha and contain a number of common features including houses of various forms and sizes, ceremonial structures, plazas, and fortification ditches linked with bastions. Within houses, hearths and food storage pits represent features of great interest, important for dating and gaining samples of artifacts, faunal, and botanical remains. Large features are visible to varying degrees in lidar, normal light or thermal infrared aerial imagery, or in site-wide electrical resistivity data, while hearths and storage pits are detectable through magnetometry. This paper explores whether such features be extracted and automatically classified through computer operations alone.
The GIS toolbox offers unrealized potential for the identification of archaeological features in such data, simply because few investigators have attempted to do so. The focus here is on how relatively common GIS tools can be employed for the identification of specific archaeological feature types that exist in Great Plains villages using remotely sensed data. Pre-processing employs image manipulation tools (low and high-pass filters) to simplify noisy data and remove local geological or topographical trends, while Fourier methods isolate and remove periodicities (e.g., plow marks that obscure the archaeological signal). Reclassification tools permit definitions of anomalous objects or potential features. Shape indices give their approximate shapes, their sizes may be calculated, and proximities between them may be determined (though “distance” modules); the last permits realizations of context. Custom filters may be designed to recognize complex shapes through pattern matching approaches. Using these tools, pathways are developed for each of the previously cited feature types of archaeological interest. Collectively, they offer a diverse array of decision making mechanisms for the identification and classification of complex archaeological features.


Karsten Lambers

Associate Professor, Archaeological Computer Science, Leiden University
avatar for Arianna Traviglia

Arianna Traviglia

MC+1 Research Fellow, University Ca'Foscari of Venice

avatar for Kenneth L Kvamme

Kenneth L Kvamme

Professor, University of Arkansas
I enjoy quantitative & technological applications in archaeology

Thursday March 31, 2016 11:20 - 11:45 CEST
Domus Bibliotheca