Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data

This paper presents an innovative multisensor, multitemporal machine-learning approach using remote sensing big data for the detection of archaeological mounds in Cholistan (Pakistan). The Cholistan Desert presents one of the largest concentrations of Indus Civilization sites (from ca. 3300 to 1500 BC). Cholistan has figured prominently in theories about changes in water availability, the rise and decline of the Indus Civilization, and the transformation of fertile monsoonal alluvial plains into an extremely arid margin. This paper implements a multisensor, multitemporal machine-learning approach for the remote detection of archaeological mounds. A classifier algorithm that employs a large-scale collection of synthetic-aperture radar and multispectral images has been implemented in Google Earth Engine, resulting in an accurate probability map for mound-like signatures across an area that covers ca. 36,000 km2. The results show that the area presents many more archaeological mounds than previously recorded, extending south and east into the desert, which has major implications for understanding the archaeological significance of the region. The detection of small (30 ha) suggests that there were continuous shifts in settlement location. These shifts are likely to reflect responses to a dynamic and changing hydrological network and the influence of the progressive northward advance of the desert in a long-term process that culminated in the abandonment of much of the settled area during the Late Harappan period.This research arises from a collaboration between the European Research Council (ERC) Consolidator Grant TwoRains project (H.A.O., C.A.P., and A.S.G.) and the Marie Skłodowska-Curie Actions Individual Fellowship MarginScapes (F.C.C.) and WaMStrIn (A.G.-M.). TwoRains is funded by the ERC under the European Union’s Horizon 2020 research and innovation program (grant agreement 648609) and the Department of Science & Technology/UK-India Education and Research Initiative, the British Academy, and the McDonald Institute for Archaeological Research. Margin-Scapes and WaMStrIn are funded under the European Commission Marie Sklodowska-Curie grant agreements 794711 and 746446, respectively. H.A.O. is a Ramón y Cajal Fellow (Spanish Ministry of Science, Innovation and Universities), and A.S.G. is a member of the Global Challenges Research Fund TIGR2ESS project (BB/P027970/1)