论文标题
大规模的自动回归街道网络建模
Large-Scale Auto-Regressive Modeling Of Street Networks
论文作者
论文摘要
我们提出了一种新颖的生成方法，用于创建城市规模的路布局。虽然最近方法的输出在覆盖区域的大小和多样性的尺寸上都受到限制，但我们的框架可产生大量的高质量遍历图，这些图形由顶点和边缘组成，这些边缘和边缘代表了覆盖400平方公里或更多的完整街道网络。尽管我们的框架可以处理一般的2D嵌入式图，但由于培训数据的广泛可用性，我们专注于街道网络。 我们的生成框架由以滑动窗口方式使用的变压器解码器组成，以预测索引字段，每个索引编码本地邻域的表示形式。每个索引的语义由上下文向量的字典确定。然后将索引字段输入到解码器以计算街道图。 使用OpenStreetMap的数据，我们在整个城市甚至在美国等大国中训练我们的系统，并最终将其与最新的状态进行比较。
We present a novel generative method for the creation of city-scale road layouts. While the output of recent methods is limited in both size of the covered area and diversity, our framework produces large traversable graphs of high quality consisting of vertices and edges representing complete street networks covering 400 square kilometers or more. While our framework can process general 2D embedded graphs, we focus on street networks due to the wide availability of training data. Our generative framework consists of a transformer decoder that is used in a sliding window manner to predict a field of indices, with each index encoding a representation of the local neighborhood. The semantics of each index is determined by a dictionary of context vectors. The index field is then input to a decoder to compute the street graph. Using data from OpenStreetMap, we train our system on whole cities and even across large countries such as the US, and finally compare it to the state of the art.
