AI 中文总结
本文对比分析式与基于Eduard的神经地貌晕渲在卡尔加里市中心的性能，探索山地训练的神经晕渲适配城市环境的可能性，明确其优劣场景并提出未来方向。
AI 中文摘要
本文使用卡尔加里市中心的高分辨率数字高程模型（DEM）和数字表面模型（DSM）数据，探究分析式晕渲方法与基于神经的晕渲方法在密集城市环境中的性能。本研究将单方向与多方向分析式晕渲，与在Eduard中生成的地貌晕渲进行对比；Eduard是一个机器学习系统，最初开发用于模拟瑞士风格的晕渲地貌，其训练数据主要为山地景观。由于Eduard并非为建筑物、桥梁、街道、树木及其他城市基础设施设计，因此核心问题并非它是否能完美复现城市形态，而是参数调整能否在与传统分析式方法相比时，产生视觉效果良好、制图有用，甚至在某些情况下更优的结果。分析尤其关注地形类型、微观与宏观概括，以及平坦区域细节参数，同时在整个神经实验中保持大规模晕渲风格一致。本文结构为探索性对比，而非通用最佳实践的基准测试，旨在明确分析式晕渲仍更可靠的场景、Eduard具有意外优势的场景，以及神经晕渲因训练偏向山地地形而失效的场景。本研究通过测试专为自然地貌设计的神经方法能否适配高度建成的城市环境，为当前地形表示领域的研究做出贡献，并在结论中提出未来应针对城市地貌晕渲开展专门的模型训练与评估。
英文摘要
This article explores the performance of analytical and neural-based hillshading methods in a dense urban environment using high-resolution digital elevation model (DEM) and digital surface model (DSM) data for downtown Calgary. The study compares single-direction and multi-direction analytical hillshading with relief shading generated in Eduard, a machine-learning system originally developed to emulate Swiss-style shaded relief trained primarily on mountainous landscapes. Because Eduard was not designed for buildings, bridges, streets, trees, and other urban infrastructures, the central question is not whether it perfectly reproduces urban morphology, but whether parameter tuning can nevertheless produce visually strong, cartographically useful, and in some cases superior results when compared with conventional analytical methods. The analysis focuses especially on terrain type, micro and macro generalization, and flat-area detail parameters, while keeping the large-scale shading style constant throughout the neural experiments. The article is structured as an exploratory comparison rather than a benchmark of universal best practice. It aims to identify where analytical hillshading remains more reliable, where Eduard offers unexpected strengths, and where neural shading fails because of its training bias toward alpine terrain. The study contributes to current work on terrain representation by testing whether a neural approach designed for natural landforms can be adapted to a highly built urban setting, and it concludes by arguing for future model training and evaluation specifically targeted at urban relief shading.
Comments15 pages, 16 figures, submitted for publication
