Remote and Close Range Sensing for the Automatic Identification and Characterization of Archaeological Looting. The Case of Peru

Abstract

Looting is the major source of artefacts for the antiquities market. Specific measures are needed to fight the whole chain of the illicit activities undertaken by criminal organizations (from the excavation to the selling of the artefacts), and they should be devised for each phase of such illegal activities. The development and use of appropriate technologies for the identification of the most 'vulnerable' sites, and the timely detection and automatic quantification of the extension of the looted areas are crucial steps for setting up a monitoring system working also for remote and inaccessible archaeological areas, often in regions affected by armed conflicts or characterized by flight restrictions. In this context, Earth Observation (EO) technologies can provide reliable information: (i) to quantify the looting phenomenon even if it is on an 'industrial scale' over large areas, and (ii) to set up a systematic monitoring tool to trace the illicit trade in antiquities.

An assessment of high temporal frequency satellite data for historic environment applications. A case study from Scotland

“…The role of satellite data for monitoring heritage across the globe is well‐established, often in the context of identifying looting and urban development that is damaging archaeological sites and landscapes (e.g., Agapiou et al, 2017; Masini & Lasaponara, 2021; Tapete & Cigna, 2019). A more specific requirement for Historic Environment Scotland is fulfilling its obligations to monitor the condition of over 8000 monuments that are designated (scheduled) as being of national importance (HES, 2018).…”

“…The role of satellite data for monitoring heritage across the globe is well-established, often in the context of identifying looting and urban development that is damaging archaeological sites and landscapes (e.g., Agapiou et al, 2017;Masini & Lasaponara, 2021;Tapete & Cigna, 2019). A more specific requirement for Historic Environment…”

The Black Desert Drone Survey: New Perspectives on an Ancient Landscape

“…Given the difficulties inherent in identification of poorly understood ancient features, and the vicissitudes of imagery (e.g., shadows, blurry areas of imagery), this option seems the more reliable path for this project. Were this project a little bigger, it would have made sense to try to utilize a semiautomated classification system (e.g., [52][53][54][55]) however, given the scale of the survey, and the noise in the data, we opted for a manual analysis.…”

Unstructured satellite survey detects up to 20% of archaeological sites in coastal valleys of southern Peru

“…Scholars have also begun to extend the utility of satellite imagery with machine learning techniques, training computers to automate the process of site discovery [25][26][27]. The approach promises efficient identification of sites, particularly in regions where it may be costly or impossible to survey using conventional methods [17,24,28]. However, these types of automated satellite surveys have a number of limitations, and previous research demonstrates that the most effective machine learning programs are still outperformed by teams of experts manually combing through large volumes of high resolution satellite imagery [28,29].…”