论文标题
SAT2Graph：通过图形编码的道路图提取
Sat2Graph: Road Graph Extraction through Graph-Tensor Encoding
论文作者
论文摘要
从卫星图像中推断道路图是一项具有挑战性的计算机视觉任务。先前的解决方案分为两类：（1）基于像素细分的方法，这些方法预测每个像素是否在道路上，以及（2）基于图的方法，这些方法可以在迭代上进行预测。我们发现，这两种方法具有互补的优势，同时遭受了自己固有的局限性。 在本文中，我们提出了一种新方法SAT2Graph，该方法将两个先前类别的优势结合在一起，成为一个统一的框架。 SAT2Graph中的关键思想是一种新颖的编码方案，即图形编码（GTE），该方案将道路图编码为张量表示。 GTE使得可以训练一个简单的，非电流的监督模型，以预测直接从图像捕获图形结构的丰富功能。我们使用两个大数据集评估SAT2Graph。我们发现SAT2Graph超过了两个广泛使用的指标，TOPO和APLS的先前方法。此外，尽管先前的工作只能渗透平面路图，但我们的方法能够推断出堆叠的道路（例如立交桥），并且如此强大。
Inferring road graphs from satellite imagery is a challenging computer vision task. Prior solutions fall into two categories: (1) pixel-wise segmentation-based approaches, which predict whether each pixel is on a road, and (2) graph-based approaches, which predict the road graph iteratively. We find that these two approaches have complementary strengths while suffering from their own inherent limitations. In this paper, we propose a new method, Sat2Graph, which combines the advantages of the two prior categories into a unified framework. The key idea in Sat2Graph is a novel encoding scheme, graph-tensor encoding (GTE), which encodes the road graph into a tensor representation. GTE makes it possible to train a simple, non-recurrent, supervised model to predict a rich set of features that capture the graph structure directly from an image. We evaluate Sat2Graph using two large datasets. We find that Sat2Graph surpasses prior methods on two widely used metrics, TOPO and APLS. Furthermore, whereas prior work only infers planar road graphs, our approach is capable of inferring stacked roads (e.g., overpasses), and does so robustly.
