Neural Turtle Graphics for Modeling City Road Layouts
arXiv:1910.02055
Abstract
We propose Neural Turtle Graphics (NTG), a novel generative model for spatial graphs, and demonstrate its applications in modeling city road layouts. Specifically, we represent the road layout using a graph where nodes in the graph represent control points and edges in the graph represent road segments. NTG is a sequential generative model parameterized by a neural network. It iteratively generates a new node and an edge connecting to an existing node conditioned on the current graph. We train NTG on Open Street Map data and show that it outperforms existing approaches using a set of diverse performance metrics. Moreover, our method allows users to control styles of generated road layouts mimicking existing cities as well as to sketch parts of the city road layout to be synthesized. In addition to synthesis, the proposed NTG finds uses in an analytical task of aerial road parsing. Experimental results show that it achieves state-of-the-art performance on the SpaceNet dataset.
arXiv commentICCV-2019 Oral
References in corpus (6)
- Adam: A Method for Stochastic Optimization
- Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
- DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images
- Learning Deep Generative Models of Graphs
- Topological Map Extraction from Overhead Images