2020/07/20 by Héctor A. Orengo, Francesc C. Conesa, Arnau Garcia‐Molsosa +4 · 1 citation
Earth and Planetary Sciences · Biochemistry, Genetics and Molecular Biology · #Archaeological Research and Protection #Yersinia bacterium, plague, ectoparasites research #Archaeology and ancient environmental studies #Indus #Archaeology #Geology #Remote sensing #Digital elevation model #Multispectral image #Geography #Geomorphology
paper · doi:10.1073/pnas.2005583117
openalex publication_date 2020/07/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
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.