Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data
Héctor A. Orengo, Francesc C. Conesa, Arnau Garcia‐Molsosa, Agustín Lobo, Adam S. Green, Marco Madella, Cameron A. Petrie
Abstract
Significance This paper illustrates the potential of machine learning-based classification of multisensor, multitemporal satellite data for the remote detection and mapping of archaeological mounded settlements in arid environments. Our research integrates multitemporal synthetic-aperture radar and multispectral bands to produce a highly accurate probability field of mound signatures. The results largely expand the known concentration of Indus settlements in the Cholistan Desert in Pakistan ( ca . 3300 to 1500 BC), with the detection of hundreds of new sites deeper in the desert than previously suspected including several large-sized (>30 ha) urban centers. These distribution patterns have major implications regarding the influence of climate change and desertification in the collapse of the largest of the Old-World Bronze Age civilizations.