考古遗址检测人类-人工智能合作工作流程
Luca Casini1, Nicolò Marchetti2, Andrea Montanucci1
1Department of Computer Science and Engineering, University of Bologna, Bologna, Italy.
Scientific reports
|May 29, 2023
概括
深度学习模型使用卫星图像准确地检测美索不达米亚洪水平原的考古遗址. 人与人工智能的协作增强了考古调查和数据精细化,为未来的发现做好准备.
科学领域:
- 考古学是一种科学.
- 计算机科学 计算机科学
- 遥感是一种远程传感.
背景情况:
- 在诸如美索不达米亚的洪水平原等复杂环境中发现考古遗址是具有挑战性的.
- 深度学习为大规模卫星图像的自动分析提供了潜力.
研究的目的:
- 为考古遗址检测评估预训练的语义细分模型.
- 探索人类-人工智能合作工作流程,用于考古数据分析.
主要方法:
- 用卫星图像和矢量形状注释微调预训练的深度学习模型.
- 利用大量考察的考古遗址来创建数据集.
- 随机测试用于评估模型检测准确度.
主要成果:
- 性能最好的模型实现了大约80%的检测准确度.
- 领域专业知识对于数据集构建和预测评估至关重要.
- 即使是不完美的预测在被考古学家解释时也是有价值的.
结论:
- 深度学习模型显示了考古遗址检测的重大前景.
- 一种协作的人类-人工智能方法可以优化考古调查和数据管理.
- 未来的工作流程涉及人工智能驱动的预测改进和人类专家验证.
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