超越大哥地区:使用深度学习在卫星图像中对哥时期的水库进行自动大规模绘制
Jürgen Landauer1, Sarah Klassen2,3, Adam P Wijker4
1Landauer Research, Ludwigsburg, Germany.
PloS one
|March 26, 2025
概括
这项研究使用深度学习来绘制柬埔寨哥时期的水库,提高考古遗址的识别. 人工智能模型显著加快了考古学家研究古代城市主义的景观绘制速度.
科学领域:
- 考古学遥感 考古学遥感
- 在考古学中的人工智能.
- 东南亚的考古学
背景情况:
- 高分辨率卫星图像对于识别考古遗址至关重要.
- 大哥地区已被广泛地绘制,但周边地区仍未得到充分研究.
- 手动绘制大面积的地图是耗时和劳动密集的.
研究的目的:
- 开发和评估一个深度学习模型来识别哥时期的水库.
- 加强和加快在哥文化领域的考古绘制过程.
- 为景观考古学家和研究人员提供一个有价值的工具.
主要方法:
- 使用最先进的深度学习模型 (Deeplab V3+) 进行语义细分.
- 训练模型在卫星图像中识别特有的哥尔时期的水库.
- 应用该模型来绘制大哥地区以外的考古特征.
主要成果:
- 深度学习模型在识别哥尔时期的水库方面表现出了很高的准确性.
- 人工智能模型显著增强和加快了考古学家的绘图过程.
- 该模型被证明是对景观解释的有价值的"第二意见".
结论:
- 深度学习语义细分是考古绘图的有效工具.
- 这种人工智能方法可以大幅提高考古学家的生产力.
- 该方法可适应在全球范围内识别各种特征,有助于研究密集的卫星图像分析.
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