AI-ming backwards: Vanishing archaeological landscapes in Mesopotamia and automatic detection of sites on CORONA imagery

TL;DR

This study enhances AI-based archaeological site detection by retraining a deep learning model with historic CORONA satellite imagery, leading to improved accuracy and discovery of previously unrecognized sites in Mesopotamia.

Contribution

The paper demonstrates that integrating historic CORONA imagery into AI models significantly improves archaeological site detection and enables discovery of new sites.

Findings

Detection accuracy exceeded 90% in identifying archaeological sites.

The model identified four new archaeological sites confirmed by fieldwork.

Using historic imagery reveals sites no longer visible today.

Abstract

By upgrading an existing deep learning model with the knowledge provided by one of the oldest sets of grayscale satellite imagery, known as CORONA, we improved the AI model attitude towards the automatic identification of archaeological sites in an environment which has been completely transformed in the last five decades, including the complete destruction of many of those same sites. The initial Bing based convolutional network model was retrained using CORONA satellite imagery for the district of Abu Ghraib, west of Baghdad, central Mesopotamian floodplain. The results were twofold and surprising. First, the detection precision obtained on the area of interest increased sensibly: in particular, the Intersection over Union (IoU) values, at the image segmentation level, surpassed 85 percent, while the general accuracy in detecting archeological sites reached 90 percent. Second, our retrained model allowed the identification of four new sites of archaeological interest (confirmed through field verification), previously not identified by archaeologists with traditional techniques. This has confirmed the efficacy of using AI techniques and the CORONA imagery from the 1960 to discover archaeological sites currently no longer visible, a concrete breakthrough with significant consequences for the study of landscapes with vanishing archaeological evidence induced by anthropization