GRE and Beyond: A Global Road Extraction Dataset

Accurate and timely road mapping that describes the road network geometry and topology is the key element of intelligent transport systems and smart city management. However, current global road maps like OpenStreetMap (OSM) are typically outdated and spatially incomplete with uneven accuracies. Although the development of remote sensing satellite technology and the advance of computer vision technology have made it possible to quickly extract road networks from massive very-high-resolution (VHR) remote sensing imagery, existing road extraction methods are limited by the problem: lacking of an accurate and diverse training dataset for global-scale road extraction, and manually labelling millions of road samples for training a global model is labor intensive. To address this problem, we utilized VHR satellite imagery and open-source crowdsourcing geospatial big data to build a robust global-scale road training dataset, termed GlobalRoadNet, for global road extraction (GRE) and beyond. The proposed GlobalRoadNet contains 47210 samples from 121 capital cities of six continents in Europe, Africa, Asia, South America, Oceania, and North America. Experimental results show that GlobalRoadNet can significantly improve model performance, not only can be applied for road extraction, but also has the potential to update OSM road data.