Abstract
Abstract
As computer vision becomes more popular in archaeology, it is imperative to develop best practices that balance the interpretive and analytically critical tendencies of manual survey with the systemization and efficiency promised by machine learning. Using a case study from the northern US Southwest, we demonstrate an iterative image classification approach that reflects the methodological processes that play out in field research and preserves the role of humans as nuanced decision-makers. We develop a model to identify ancestral Pueblo residential sites and use it to survey an area larger than 16,000 km
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. The semiautomated survey identified 4,905 likely archaeological features, marking one of the largest remote archaeological surveys in North America. Results not only emphasize the value of integrated computer vision for archaeological survey and site prediction but also demonstrate a research design that capitalizes on the computational value of computer vision while maintaining active engagement by the researcher.