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<p>Ancient cultural ruins, as tangible evidence of human-environment interaction, provide invaluable historical insights and critical resources for addressing global challenges like climate change and population growth. However, most ruins are indistinguishable and challenging to identify, as they have merged with the surrounding sediments through prolonged natural accumulation and anthropogenic activities. Using SNV in conjunction with the ResNet50 model, this study proposes a novel method for achieving high-precision classification of spectral data from ancient human ruins. The ResNet50 model achieves a classification accuracy of 94.86% when used independently, whereas the accuracy improves to 96.60% when the ResNet50 model is combined with SNV. The model exhibits exceptional classification performance, even when trained with a limited number of spectral image samples from ancient ruins. The superiority of the SNV + ResNet50 model provides a pioneering and effective method for the rapid and accurate identification of ancient human relics using visible-near-infrared spectroscopy.</p>

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Enhancing ancient human ruins classification with residual neural networks using visible near-infrared spectra

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月23日

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  • Rongji Luo,
  • Panpan Chen,
  • Hongtao Wang,
  • Deyang Jiang,
  • Zhen Wang,
  • Peng Lu

摘要

Ancient cultural ruins, as tangible evidence of human-environment interaction, provide invaluable historical insights and critical resources for addressing global challenges like climate change and population growth. However, most ruins are indistinguishable and challenging to identify, as they have merged with the surrounding sediments through prolonged natural accumulation and anthropogenic activities. Using SNV in conjunction with the ResNet50 model, this study proposes a novel method for achieving high-precision classification of spectral data from ancient human ruins. The ResNet50 model achieves a classification accuracy of 94.86% when used independently, whereas the accuracy improves to 96.60% when the ResNet50 model is combined with SNV. The model exhibits exceptional classification performance, even when trained with a limited number of spectral image samples from ancient ruins. The superiority of the SNV + ResNet50 model provides a pioneering and effective method for the rapid and accurate identification of ancient human relics using visible-near-infrared spectroscopy.