A novel machine learning automated change detection tool for monitoring disturbances and threats to archaeological sites

Abstract

Archaeological sites across the globe are facing significant threats and heritage managers are under increasing pressure to monitor and preserve these sites. Since 2015, the EAMENA project has documented more than 200,000 archaeological sites and the disturbances and threats affecting them across the Middle East and North Africa (MENA) region, using a combination of remote sensing, digitization, and fieldwork methodologies. The large number of sites and their often remote or otherwise difficult to access locations makes consistent and regular monitoring of these sites for disturbances and threats a daunting task. Combined with the increasing frequency and severity of threats to archaeological sites, the need to develop novel tools and methods that can rapidly monitor the changes at and around archaeological sites and provide accurate and consistent monitoring has never been more urgent. In this paper, we introduce the EAMENA Machine Learning Automated Change Detection tool (EAMENA MLACD). This newly-developed online tool uses bespoke machine learning algorithms to process sequential satellite images and create land classification maps to detect and identify disturbances and threats in the vicinity of known archaeological sites for the purposes of heritage monitoring and preservation. Initial testing and validation of results from the EAMENA MLACD in a case study in Bani Walid, Libya, demonstrate how it can be used to identify disturbances and potential threats to heritage sites, and increase the speed and efficiency of monitoring activities undertaken by heritage professionals.