Recent and Past Archaeological Looting by Satellite Remote Sensing: Approach and Application in Syria
Masini N., Lasaponara R.
machine learning archaeological lotting satellite remote sensing Syria
Illegal excavations represent one of the main risks which affect archaeological heritage throughout the world. Actions oriented to quantify damage and prevent looting can be supported by satellite technologies which can provide reliable information to detect and map devastation phenomenon in particular for remote or non-accessible sites. In these cases, it is desirable to use satellite-based semiautomatic or automatic approaches for the mapping and quantification of looting patterns. In this paper, an automatic method for archaeological looting feature extraction approach (ALFEA) has been applied to an archaeological site in Syria, Tell Sheikh Hamad, affected by archaeological looting before and during the civil war. The aim is to evaluate the capability of ALFEA to extract past and recent looting features and patterns using Google Earth images. The results have been assessed through visual inspection, which shows that the rate of success was higher than 90% for recent looting and around the 80% for past archaeological disturbance.
Source: Remote Sensing for Archaeology and Cultural Landscapes, edited by Hadjimitsis D.G.; Themistocleous K.; Cuca B.; Agapiou A.; Lysandrou V.; Lasaponara R.; Masini N.; Schreier G., pp. 123–137. Heidelberg: Springer-Verlag berlin, 2020
Publisher: Springer-Verlag berlin, Heidelberg, DEU
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@inbook{oai:it.cnr:prodotti:409335,
title = {Recent and Past Archaeological Looting by Satellite Remote Sensing: Approach and Application in Syria},
author = {Masini N. and Lasaponara R.},
publisher = {Springer-Verlag berlin, Heidelberg, DEU},
doi = {10.1007/978-3-030-10979-0_8},
booktitle = {Remote Sensing for Archaeology and Cultural Landscapes, edited by Hadjimitsis D.G.; Themistocleous K.; Cuca B.; Agapiou A.; Lysandrou V.; Lasaponara R.; Masini N.; Schreier G., pp. 123–137. Heidelberg: Springer-Verlag berlin, 2020},
year = {2020}
}