Construction of a neural network model for small-scale shaded relief maps constrained by topographic feature lines
Traditional manual methods for generating shaded relief maps can effectively highlight major topographic structures but are time-consuming and require professional skills. Analytical shading methods are faster but often lead to maps overloaded with terrain details, obscuring key topographic features...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Geocarto International |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2025.2459099 |
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| Summary: | Traditional manual methods for generating shaded relief maps can effectively highlight major topographic structures but are time-consuming and require professional skills. Analytical shading methods are faster but often lead to maps overloaded with terrain details, obscuring key topographic features, especially in Small-Scale Shaded Relief Maps (SSSR-Maps). This study focuses on the relief shading of alpine canyon terrain, introduces topographic feature lines (TFLs) as constraints, and constructs a neural network model based on Pix2pixHD, namely, TFLC-CGAN. Two generation methods, TFLC-CGAN-E and TFLC-CGAN-M, are proposed and compared. Experimental results show that TFLC-CGAN can generate SSSR-Maps with manual shading styles, simplifying terrain while preserving key features. TFLC-CGAN-E adapts better to sharply reduced TFL density, while TFLC-CGAN-M excels in feature preservation. Additionally, the relationships among digital elevation model resolution, TFL density, and the generated shaded relief map scales are explored. The proposed TFLC-CGAN offers an efficient solution for large-scale production of SSSR-Maps. |
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| ISSN: | 1010-6049 1752-0762 |