标题: Construction of a small-scale relief shading neural network model based on the attention mechanism

作者: Jiang, WP (Jiang, Wenping); Wang, Y (Wang, Yue); Ding, HJ (Ding, Haijun); Jiang, H (Jiang, Han); Xi, DP (Xi, Daping); Wang, Y (Wang, Yuan); Ma, PY (Ma, Peiyang)

来源出版物: CARTOGRAPHY AND GEOGRAPHIC INFORMATION SCIENCE  DOI: 10.1080/15230406.2025.2484209  Early Access Date: APR 2025  Published Date: 2025 APR 5  

摘要: Relief shading is a primary technique for representing the three-dimensional effects of terrain on a two-dimensional plane. This study applies deep learning to generate small-scale Swiss-style relief shading maps. An attention module is defined to focus on key information in feature maps. Based on the characteristics of relief shading and digital elevation model (DEM) data, U-Net is adjusted and optimized, resulting in the design and construction of an end-to-end relief shading neural network model (Attention Hillshading U-Net, A-UNet) built on a limited training dataset. By learning the terrain-shaping patterns from Swiss-style shading maps, the model overcomes the challenges posed by high terrain complexity and insufficient representation of landform morphology in small-scale relief shading maps. The study further investigates the impact of hyperparameters on the performance of the model in generating small-scale relief shading maps. Based on the quantitative performance of the model under different hyperparameter settings and adaptability to lower-resolution DEMs, the optimal hyperparameters for the model are determined. Additionally, experimental comparisons of small-scale relief shading map generation using A-UNet and other network models show that, compared to U-Net and its variants, A-UNet demonstrates superior adaptability to different pixel sizes, better terrain simplification, and enhanced generalization to various landform types.

作者关键词: Swiss-style relief shading; small-scale relief shading; attention mechanism; Attention Hillshading U-Net; hyperparameters

地址: [Jiang, Wenping; Wang, Yue] Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

[Ding, Haijun; Xi, Daping; Ma, Peiyang] China Univ Geosci, Sch Geog & Informat Engn, Wuhan, Peoples R China.

[Jiang, Han] Case Western Reserve Univ, Sch Engn, Cleveland, OH USA.

[Wang, Yuan] Geospatial & Nat Resources Big Data Ctr, Qinghai Prov Dept Nat Resources, Xining, Peoples R China.

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

电子邮件地址: AndyJiang@whu.edu.cn

影响因子：2.6