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

ABSTRACT: 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 G

SUBMITTER: Orengo HA 

PROVIDER: S-EPMC7414161 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature