论文标题
统一向量映射的原始图形学习
Primitive Graph Learning for Unified Vector Mapping
论文作者
论文摘要
大规模矢量映射对于运输，城市规划，调查以及人口普查很重要。我们提出了GraphMapper，这是从卫星图像中提取端到端向量图的统一框架。我们的关键思想是一种新颖的统一表示，称为“原始图”的不同拓扑的形状，这是一组形状原语及其成对关系矩阵。然后，我们将向量形状的预测，正则化和拓扑重构转换为独特的原始图学习问题。具体而言，GraphMapper是基于通过多头注意的全局形状上下文建模的通用原始图形学习网络。开发了一种嵌入式空间排序方法，用于准确的原始关系建模。我们从经验上证明了GraphMapper对两个具有挑战性的映射任务的有效性，即建立足迹正则化和道路网络拓扑重建。我们的模型在公共基准上的两个任务中都优于最先进的方法。所有代码将公开可用。
Large-scale vector mapping is important for transportation, city planning, and survey and census. We propose GraphMapper, a unified framework for end-to-end vector map extraction from satellite images. Our key idea is a novel unified representation of shapes of different topologies named "primitive graph", which is a set of shape primitives and their pairwise relationship matrix. Then, we convert vector shape prediction, regularization, and topology reconstruction into a unique primitive graph learning problem. Specifically, GraphMapper is a generic primitive graph learning network based on global shape context modelling through multi-head-attention. An embedding space sorting method is developed for accurate primitive relationship modelling. We empirically demonstrate the effectiveness of GraphMapper on two challenging mapping tasks, building footprint regularization and road network topology reconstruction. Our model outperforms state-of-the-art methods in both tasks on public benchmarks. All code will be publicly available.
