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<p>As a living heritage, the Jiangnan Canal landscape features interwoven natural and artificial water networks. To accurately interpret its overall spatial composition and address heritage sustainability challenges, this study proposes Geo-SegFormer: a framework for the automated segmentation of historical landscapes by integrating deep learning with multimodal geospatial data. Using a self-constructed dataset, the proposed method achieves the first 1-meter-resolution reconstruction of the entire Jiangnan Canal landscape system across 35 categories, surpassing traditional manual methods in coverage, diversity, and precision. Quantitative analysis reveals the contribution weights to the segmentation: DEM 51.6%, hydrographic data 17.7%, and imagery data 30.7%, demonstrating that the canal landscape is structured upon natural terrain, with water networks as its spatial framework. This outcome establishes a critical data foundation for heritage conservation and interdisciplinary spatial quantitative research. Beyond this specific case, the developed methodology also possesses considerable potential for cross-regional application.</p>

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Segmenting of historic landscape system along Jiangnan Canal based on deep learning and multimodal geodata

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月31日

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  • Li Ran,
  • Keyu Chen,
  • Shunhan Zhang,
  • Qianting Gao,
  • Yuqi Gao,
  • Shangyu Tan,
  • Qing Lin

摘要

As a living heritage, the Jiangnan Canal landscape features interwoven natural and artificial water networks. To accurately interpret its overall spatial composition and address heritage sustainability challenges, this study proposes Geo-SegFormer: a framework for the automated segmentation of historical landscapes by integrating deep learning with multimodal geospatial data. Using a self-constructed dataset, the proposed method achieves the first 1-meter-resolution reconstruction of the entire Jiangnan Canal landscape system across 35 categories, surpassing traditional manual methods in coverage, diversity, and precision. Quantitative analysis reveals the contribution weights to the segmentation: DEM 51.6%, hydrographic data 17.7%, and imagery data 30.7%, demonstrating that the canal landscape is structured upon natural terrain, with water networks as its spatial framework. This outcome establishes a critical data foundation for heritage conservation and interdisciplinary spatial quantitative research. Beyond this specific case, the developed methodology also possesses considerable potential for cross-regional application.