利用VGI数据更新道路网络的深度学习方法

Prajowal Manandhar, P. Marpu, Z. Aung
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引用次数: 1

摘要

在我们早期的工作中,我们的工作是通过在志愿地理信息(VGI)引导的方向上穿越的代理来提取道路总宽度。VGI方法的唯一缺点是它无法更新新的道路发展。在本文中,我们引入了深度学习方法来更新道路网络。我们利用之前工作的输出作为输入来训练卷积神经网络(CNN)。然后进行进一步的后处理,去除CNN输出上的非道路段(如建筑物、植被等),最后得到更新后的路线图。
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Deep Learning Approach To Update Road Network using VGI Data
In our earlier work, we worked on extraction of the total width of road by agents traversing in the direction guided by Volunteered Geographic Information (VGI). The only downfall of VGI approach is its inability to update the new road developments. In this paper, we introduce deep learning approach to update the road network. We make use of the output of our previous work which forms as an input to train the Convolutional Neural Network (CNN). Then, further post processing is performed to remove non-road segments (such as buildings, vegetation, etc) on the output of CNN and finally, obtain the updated road map.
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