A PATH GUIDED METHOD FOR QUICKLY LABELING ROADS IN SATELLITE IMAGES

Abstract

Determining roads from satellite images has gained more research interest after the recent progress on data-heavy machine learning methods which are also accelerated by the increased amounts of accessible data. An important challenge of learning-based approaches is obtaining labeled data to train the systems. In this study, we propose a method for quickly labeling roads over satellite images of any desired location. Our method exploits the 2D path information obtained from OpenStreetMap, an online community-contributed source of geolocated information. In this environment, roads are roughly described as line segments without their exact shapes and sizes. Using this rough information, we propose a simple interactive user interface where users easily label the road boundaries over presented satellite images. Using our approach, it is possible to rapidly label regions with different road characteristics. Such an approach allows for training separate machine learning systems for different parts of the world which would be advantageous over training a single system to identify all kinds of roads.

Keywords

• City Modeling
• Satellite images
• OpenStreetMap
• Labeling
• Machine Learning

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