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 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 (<5 ha) to large mounds (>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.

Killing the Priest-King: Addressing Egalitarianism in the Indus Civilization

“…The same may be true of Ganweriwala, though (Nath 1998(Nath , 1999(Nath , 2001). Extent polygons generated from site plans using QGIS 3.10 and then added as insets with Google Earth imagery (accessed 2020) to a basemap derived from Figure 1 only survey data are available (e.g., Masih 2018;Mughal 1997), and there are multiple sites in the surrounding area that appear to be equally extensive (e.g., Orengo et al 2020). Harappa's elevated mounds were established at different points in time but were all occupied beginning around 2600 BC (Kenoyer 2008;Meadow andKenoyer 1997, 2005;Wright 2010).…”

Detecting Change at Archaeological Sites in North Africa Using Open-Source Satellite Imagery

“…Given this, Google Earth Engine is increasingly being used for archaeological research, including by EAMENA [1]. Other research has focused on using it for detection of sites and landscape features [27,28], with its potential for heritage management first raised by Agapiou [18]. There are limitations to Google Earth Engine, including restrictions on asset and download sizes, but at present, it offers the best option for heritage professionals to access data and analysis-power for change detection in a standardised and open-source way.…”

Google Earth Engine as Multi-Sensor Open-Source Tool for Supporting the Preservation of Archaeological Areas: The Case Study of Flood and Fire Mapping in Metaponto, Italy

“…The number of papers about GEE [ 26 , 30 , 31 , 32 ] in recent years has increased exponentially in several fields, such as: (i) forest and vegetation [ 33 , 34 ]; (ii) land use and land cover [ 35 , 36 , 37 ]; (iii) hydrology [ 38 , 39 , 40 ]; (iv) ecosystems and sustainability [ 41 ]; (v) agriculture [ 42 , 43 , 44 , 45 ]; (vi) climate [ 46 ]; (vii) urban sprawl [ 47 ]; (viii) hazards [ 48 , 49 ]; (ix) cultural heritage [ 50 ]. The use of GEE has allowed multiple tools to be created and shared for free.…”