标题: Integrating morphological knowledge of contour data and graph neural network for landform type recognition

作者: Kong, B (Kong, Bo); Ai, TH (Ai, Tinghua); Yang, M (Yang, Min); Wu, H (Wu, Hao); Yan, XF (Yan, Xiongfeng); Wang, YQ (Wang, Yongquan); Yu, HF (Yu, Huafei)

来源出版物: INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE DOI: 10.1080/13658816.2025.2455075 Early Access Date: JAN 2025 Published Date: 2025 JAN 21

摘要: Landform type recognition presents significant implications for understanding landform origins, evolutionary mechanisms, and morphological differences. Artificial intelligence (AI) techniques based on sample learning often lead to unsatisfactory outcomes due to the intricate genesis and regional heterogeneity of landforms. This study combines domain knowledge with a deep learning (DL) model to improve landform type recognition. Contour data serves as a valuable resource, offering rich morphological information across horizontal, vertical, local, and macro scales. Our approach incorporated morphological knowledge and proximity relationships derived from contours into a graph convolutional network using the DiffPool technique (GCN-DP). Guided by the First Law of Geography, contours within each landform unit were represented as graphs, incorporating morphological knowledge as node features. The GCN-DP model then employed convolution and pooling to extract hierarchical features from these graphs for landform type recognition. A performance evaluation demonstrated the effectiveness of our method with an F1-score of 87.40%, surpassing RF and GCN methods by 5.24-12.50%, respectively. Ablation experiments confirmed the usefulness of morphological knowledge. This study offers an efficient strategy for landform type recognition, improving the level of intelligent mining using contour data.

作者关键词: Landform type recognition; contour data; morphological knowledge; graph neural network

KeyWords Plus: CLASSIFICATION; TERRAIN; EXTRACTION; VALLEY; LINES

地址: [Kong, Bo; Ai, Tinghua; Yang, Min; Wu, Hao; Wang, Yongquan; Yu, Huafei] Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

[Yan, Xiongfeng] Tongji Univ, Coll Surveying & Geoinformat, Shanghai, Peoples R China.

通讯作者地址: Ai, TH (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

电子邮件地址: tinghuaai@whu.edu.cn

影响因子：4.3