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A human-AI collaboration workflow for archaeological sites detection.

Luca Casini¹, Nicolò Marchetti², Andrea Montanucci¹

¹Department of Computer Science and Engineering, University of Bologna, Bologna, Italy.

Scientific Reports

|May 29, 2023

Summary

Deep learning models accurately detect archaeological sites in Mesopotamian floodplains using satellite imagery. Human-AI collaboration enhances archaeological survey and data refinement for future discoveries.

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Area of Science:

Archaeological science
Computer science
Remote sensing

Background:

Archaeological site detection in complex environments like Mesopotamian floodplains is challenging.
Deep learning offers potential for automated analysis of large-scale satellite imagery.

Purpose of the Study:

To evaluate pre-trained semantic segmentation models for archaeological site detection.
To explore a Human-AI collaboration workflow for archaeological data analysis.

Main Methods:

Fine-tuning pre-trained deep learning models with satellite imagery and vector shape annotations.
Utilizing a large corpus of surveyed archaeological sites for dataset creation.
Randomized testing to assess model detection accuracy.

Main Results:

The best-performing model achieved approximately 80% detection accuracy.
Domain expertise was vital for dataset construction and prediction evaluation.
Even imperfect predictions are valuable when interpreted by archaeologists.

Conclusions:

Deep learning models show significant promise for archaeological site detection.
A collaborative Human-AI approach can optimize archaeological surveys and data management.
Future workflows involve AI-driven prediction refinement and human expert validation.