用60年代卫星图训练AI，发现4处新考古遗址
AI-ming backwards: Vanishing archaeological landscapes in Mesopotamia and automatic detection of sites on CORONA imagery
- 用CORONA历史影像重训卷积网络，提升考古遗址识别能力
- 图像分割IoU超85%，遗址检测准确率达90%
- 发现4处新遗址，适合研究被人类活动破坏的古地貌
通过将最古老的一组灰度卫星影像CORONA的知识融入现有深度学习模型，提升了人工智能在近五十年来发生巨大变化的美索不达米亚地区自动识别考古遗址的能力，该地区许多遗址已被完全摧毁。初始基于Bing的卷积神经网络模型在巴格达以西、中美索不达米亚冲积平原的阿布格莱布地区，使用CORONA影像重新训练。结果出人意料：首先，目标区域的检测精度显著提升，图像分割层面的交并比（IoU）超过85%，遗址识别总体准确率达90%；其次，重训模型成功识别出4处新的考古遗址（经实地验证），是传统方法未能发现的。这证实了利用1960年代的CORONA影像与AI结合，可有效发现当前已不可见的考古遗迹，为受人为活动影响而逐渐消失的景观研究带来实质性突破。
原文摘要 · Abstract (English)
By upgrading an existing deep learning model with the knowledge provided by one of the oldest sets of grayscale satellite imagery, known as CORONA, we improved the AI model attitude towards the automatic identification of archaeological sites in an environment which has been completely transformed in the last five decades, including the complete destruction of many of those same sites. The initial Bing based convolutional network model was retrained using CORONA satellite imagery for the district of Abu Ghraib, west of Baghdad, central Mesopotamian floodplain. The results were twofold and surprising. First, the detection precision obtained on the area of interest increased sensibly: in particular, the Intersection over Union (IoU) values, at the image segmentation level, surpassed 85 percent, while the general accuracy in detecting archeological sites reached 90 percent. Second, our retrained model allowed the identification of four new sites of archaeological interest (confirmed through field verification), previously not identified by archaeologists with traditional techniques. This has confirmed the efficacy of using AI techniques and the CORONA imagery from the 1960 to discover archaeological sites currently no longer visible, a concrete breakthrough with significant consequences for the study of landscapes with vanishing archaeological evidence induced by anthropization
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