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Constraint-Based Evaluation of Map Images Generalized by Deep Learning

机译：基于约束的评价地图图像广义的深度学习

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摘要

Deep learning techniques have recently been experimented for map generalization. Although promising, these experiments raise new problems regarding the evaluation of the output images. Traditional map generalization evaluation cannot directly be applied to the results in a raster format. Additionally, the internal evaluation used by deep learning models is mostly based on the realism of images and the accuracy of pixels, and none of these criteria is sufficient to evaluate a generalization process. Finally, deep learning processes tend to hide the causal mechanisms and do not always guarantee a result that follows cartographic principles. In this article, we propose a method to adapt constraint-based evaluation to the images generated by deep learning models. We focus on the use case of mountain road generalization, and detail seven raster-based constraints, namely, clutter, coalescence reduction, smoothness, position preservation, road connectivity preservation, noise absence, and color realism constraints. These constraints can contribute to current studies on deep learning-based map generalization, as they can help guide the learning process, compare different models, validate these models, and identify remaining problems in the output images. They can also be used to assess the quality of training examples.
机译：最近深度学习技术地图概括的实验。有前途,这些实验提出新的问题关于评估的输出图像。传统地图概括评价不能结果直接应用于光栅格式。主要是基于使用的深度学习模型图像和像素的准确性的现实主义,和这些条件是充分的评估一个归纳的过程。学习过程倾向于隐藏的因果机制和并不总是保证结果符合制图原则。篇文章中,我们提出一个方法来适应基于评价的图像生成的深度学习模型。山路的用例泛化细节七基于栅格的约束,即混乱,降低聚结,平滑,保存位置,道路连接保存,噪音,和现实主义色彩约束。基于当前的研究深度学习地图泛化,因为它们可以帮助指导学习过程,比较不同模型,验证这些模型,确定剩余输出图像的问题。用于评估培训质量的例子。

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