Title
Cairn Detection in Southern Arabia
Speaker
Jared Schuetter, Battelle
Abstract
Excavating cairns in southern Arabia is a way for anthropologists to understand which factors led ancient settlers to transition from a pastoral lifestyle and tribal narrative to the formation of states that exist today. Locating these monuments has traditionally been done in the field, relying on eyewitness reports and costly searches through the arid landscape. In this presentation, an algorithm for automatically detecting cairns in satellite imagery will be discussed. The algorithm uses a set of filters in a window based approach to eliminate background pixels and other objects that do not look like cairns. The resulting set of detected objects constitutes fewer than 0.001 percent of the pixels in the satellite image, and contains the objects that look the most like cairns in imagery. When a training set of cairns is available, a further reduction of this set of objects can take place. This includes clustering the satellite image to determine landform classes that tend to be consistent with the presence of cairns. Due to the large number of pixels in the image, a subsample spectral clustering algorithm called Multiple Sample Data Spectroscopic clustering is used. This algorithm combines information from different samples to inform clustering results in large data situations (e.g. imagery). Finally, a step-through of the cairn detection algorithm and satellite image clustering will be shown for an area in the Hadramawt region of Yemen.