Vanishing archaeological landscapes in Mesopotamia:CORONA imagery site detection

Researchers upgraded a deep learning model using 1960s CORONA satellite imagery to automatically detect archaeological sites in Mesopotamia, achieving over 85% Intersection over Union and 90% detection accuracy in the Abu Ghraib district of Iraq. The retrained model identified four previously unknown archaeological sites, confirmed through field verification, demonstrating the efficacy of AI for discovering vanishing landscapes due to anthropization.

Computer Science Computer Vision and Pattern Recognition Submitted on 17 Jul 2025 v1 https://arxiv.org/abs/2507.13420v1 , last revised 29 Jul 2025 this version, v2 Title:AI-ming backwards: Vanishing archaeological landscapes in Mesopotamia and automatic detection of sites on CORONA imagery View PDF /pdf/2507.13420 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 Submission history From: Marco Roccetti view email /show-email/d62c3ce5/2507.13420 Thu, 17 Jul 2025 14:21:50 UTC 10,085 KB v1 /abs/2507.13420v1 v2 Tue, 29 Jul 2025 07:01:34 UTC 9,945 KB Current browse context: cs.CV References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .