# Image-Conditioned Graph Generation for Road Network Extraction Source: [Image-Conditioned Graph Generation for Road Network Extraction]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:11:27.495Z Authors: Davide Belli, Thomas Kipf Published: 2019-10-31T11:38:13.000Z ## Abstract Deep generative models for graphs have shown great promise in the area of drug design, but have so far found little application beyond generating graph-structured molecules. In this work, we demonstrate a proof of concept for the challenging task of road network extraction from image data. This task can be framed as image-conditioned graph generation, for which we develop the Generative Graph Transformer (GGT), a deep autoregressive model that makes use of attention mechanisms for image conditioning and the recurrent generation of graphs. We benchmark GGT on the application of road network extraction from semantic segmentation data. For this, we introduce the Toulouse Road Network dataset, based on real-world publicly-available data. We further propose the StreetMover distance: a metric based on the Sinkhorn distance for effectively evaluating the quality of road network generation. The code and dataset are publicly available. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Road Network Extraction on Toulouse Road Network](): Road Network Extraction; Toulouse Road Network; StreetMover: 0.0192. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()