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A fast and effective deep learning approach for road extraction from historical maps by automatically generating training data with symbol reconstruction
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-08-22 , DOI: 10.1016/j.jag.2022.102980
Chenjing Jiao, Magnus Heitzler, Lorenz Hurni

Historical road data are often needed for different purposes, such as tracking the evolution of road networks, spatial data integration, and urban sprawl investigation. However, road extraction from historical maps is challenging due to their dissatisfying quality, the difficulty in distinguishing road symbols from those of other features (e.g., isolines, streams), etc. Recently, although deep learning, especially deep convolutional neural networks (CNNs), have been successfully applied to extract roads from remote sensing images, road extraction from historical maps with deep learning is rarely seen in existing studies. Apart from this, it is time-consuming and laborious to manually label large amounts of training data. To bridge these gaps, this paper proposes a novel and efficient methodology to automatically generate training data through symbol reconstruction for road extraction. The proposed methodology is validated by implementing and comparing four training scenarios using the Swiss Siegfried map. The experiments show that imitation maps generated by symbol reconstruction are especially useful in two cases. First, if little manually labelled training data are available, models trained on imitation maps alone can already provide satisfactory road extraction results. Second, when training data from imitation maps are mixed with real training data, the resulting models even outperform the models trained on real data alone for some metrics, thus indicating that imitation maps can be a highly valuable addition. This research provides a new insight for fast and effective road extraction from historical maps using deep learning.



中文翻译:

一种快速有效的深度学习方法,通过自动生成带有符号重建的训练数据,从历史地图中提取道路

历史道路数据通常用于不同目的,例如跟踪道路网络的演变、空间数据集成和城市扩张调查。然而,从历史地图中提取道路具有挑战性,因为它们的质量不令人满意,难以将道路符号与其他特征(例如,等值线、溪流)区分开来等。最近,尽管深度学习,尤其是深度卷积神经网络(CNN) , 已成功应用于从遥感图像中提取道路,但在现有研究中很少见到使用深度学习从历史地图中提取道路。除此之外,手动标记大量训练数据既费时又费力。为了弥合这些差距,本文提出了一种新颖有效的方法,通过符号重建自动生成训练数据以进行道路提取。通过使用瑞士齐格弗里德地图实施和比较四种训练场景来验证所提出的方法。实验表明,符号重建生成的仿图在两种情况下特别有用。首先,如果几乎没有手动标记的训练数据可用,仅在模仿地图上训练的模型已经可以提供令人满意的道路提取结果。其次,当来自模仿地图的训练数据与真实训练数据混合时,生成的模型甚至在某些指标上优于仅在真实数据上训练的模型,因此表明模仿地图可能是一个非常有价值的补充。

更新日期:2022-08-23
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