Automatic road graph extraction from aerial and satellite images is a long-standing challenge. Existing algorithms are either based on pixel-level segmentation followed by vectorization, or on iterative graph construction using next move prediction. Both of these strategies suffer from severe drawbacks, in particular high computing resources and incomplete outputs. By contrast, we propose a method that directly infers the final road graph in a single pass. The key idea consists in combining a Fully Convolutional Network in charge of locating points of interest such as intersections, dead ends and turns, and a Graph Neural Network which predicts links between these points. Such a strategy is more efficient than iterative methods and allows us to streamline the training process by removing the need for generation of starting locations while keeping the training end-to-end. We evaluate our method against existing works on the popular RoadTracer dataset and achieve competitive results. We also benchmark the speed of our method and show that it outperforms existing approaches. This opens the possibility of in-flight processing on embedded devices.
翻译:从空中和卫星图像中自动绘制道路图是一项长期挑战。 现有的算法要么基于像素级分解,然后是矢量化,要么基于使用下一个移动预测的迭代图形构造。 这两种战略都存在严重的缺陷, 特别是高计算资源和不完整的产出。 相反, 我们建议一种方法, 直接从一个通道中推断最后的道路图。 关键的想法是将一个完整的革命网络结合起来, 负责查找感兴趣的点, 如交叉点、 死路和旋转点, 以及一个预测这些点之间连接的图形神经网络。 这样的战略比迭接方法更有效, 并使我们能够简化培训过程, 在保持培训端到端的同时, 消除起始点的生成需求。 我们对照流行的RoadTracger数据集的现有工程来评估我们的方法, 并取得竞争性的结果。 我们还将我们的方法的速度作为基准, 并显示它比现有方法要快。 这样就有可能在嵌入装置上进行飞行处理 。