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Daily Arxiv

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

Created by
  • Haebom

Author

Alessandro Pistola, Valentina Orru', Nicolo' Marchetti, Marco Roccetti

Outline

We retrained the existing deep learning model with CORONA satellite image data to improve the automatic identification performance of archaeological sites in the Abu Ghraib area of Iraq. Using a Bing-based convolutional neural network model, we retrained the model by considering CORONA images from 50 years ago and current terrain changes, and achieved an IoU value of over 85% and an archaeological site detection accuracy of 90%. In addition, we discovered four new archaeological sites that had not been discovered using existing methods and confirmed them through field verification. This is a groundbreaking result that shows that AI technology and old satellite images are effective in discovering now-disappeared archaeological sites.

Takeaways, Limitations

Takeaways:
Demonstrated that using old satellite imagery (CORONA) can improve the performance of deep learning models and discover lost archaeological sites.
Presenting new possibilities for archaeological research using AI technology.
By utilizing AI-based image analysis technology, we can efficiently find historical sites that are difficult to find using existing methods.
Limitations:
The study area is limited to the Abu Ghraib site. Generalizability to other regions or types of sites requires further research.
Possible loss of accuracy due to resolution and quality limitations of CORONA images.
Lack of detailed description of the model's retraining process and detailed parameters.
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