基于课程学习的策略,从印度和巴基斯坦的历史地图中检测低密度的考古丘陵
Iban Berganzo-Besga1, Hector A Orengo2,3, Felipe Lumbreras4
1Landscape Archaeology Research Group (GIAP), Catalan Institute of Classical Archaeology (ICAC), Pl. Rovellat s/n, 43003, Tarragona, Spain.
Scientific reports
|July 12, 2023
概括
本研究介绍了两个算法,用于在历史地图上自动检测考古古堡,解决诸如低特征密度和有限的训练数据等挑战. 该方法成功地在一个广的地区识别了近6000个潜在的考古遗址.
科学领域:
- 考古调查考古调查的研究.
- 计算机视觉 计算机视觉 计算机视觉
- 历史地理学的历史地理.
背景情况:
- 历史地图对于重建由现代发展改变的过去景观至关重要.
- 许多考古学特征,如山丘,在历史地图上描绘,但往往没有被认定为这样的.
- 自动化调查方法面临的挑战是特征密度低,培训数据稀缺.
研究的目的:
- 开发和评估两个算法,用于在历史地图上大规模自动检测和实例细分潜在的考古山丘.
- 解决考古特征密度低,自动调查培训数据有限的局限性.
- 提高从历史地图数据中识别考古定居点的准确性和效率.
主要方法:
- 设计了算法来检测历史地图上通过hachures和form-lines描绘的土丘特征.
- 应用课程学习策略,使用合成数据来增强特征识别.
- 集成过器用于地形设置,形状和尺寸,以改进检测准确度.
- 测试的算法在历史性的1"至1英里地图系列,覆盖470,500平方公里.
主要成果:
- 取得的回忆率为52.61%和精度为82.31%的哈奇尔丘.
- 实现了70.80%的回忆力和70.29%的精度,用于形状线.
- 成功检测到近6000个土特征,代表了这种方法的最大规模应用.
- 在与培训数据相似的地图上,证明回忆率>60%和精度>90%.
结论:
- 开发的算法提供了一个强大的解决方案,用于在历史地图上大规模的考古土检测.
- 该方法有效地应对特征密度低和培训数据有限的挑战.
- 适应潜力允许改进检测,在新的地图数据上进行最小的重新训练.
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