Computer Vision With OpenStreetMap and SpaceNet — A Comparison

Adam Van Etten
The DownLinQ
Published in
5 min readSep 9, 2019


Now that the SpaceNet 5 dataset has been released, and the challenge is live on Topcoder, we anticipate a great many insights from this challenge into how well computer vision can automatically extract road networks and travel time estimates.

In support of the SpaceNet 5 challenge, this post seeks to provide motivation as to the utility of this new dataset. We also explore some of the capabilities that the SpaceNet challenges has helped inspire. In areas such as automated road network extraction, we demonstrate that such capabilities compare favorably to current state of the art methodologies, and may be able to contribute back to OpenStreetMap (OSM) to improve labels in difficult locales. See our arXiv paper for further details, and our previous post for our algorithmic approach.

1. OSM Data

In many regions of the world OpenStreetMap (OSM) road networks are remarkably complete. Yet, in developing nations OSM labels are often missing metadata tags (such as speed limit or number of lanes), or are poorly registered with overhead imagery (i.e., labels are offset from the coordinate system of the imagery). See Section 2 of our blog on road network extraction at scale for further details. An active community works hard to keep the road network up to date, but such tasks can be challenging and time consuming in the face of large scale disasters. For example, following Hurricane Maria, it took the Humanitarian OpenStreetMap Team (HOT) over two months to fully map Puerto Rico.

2. SpaceNet Dataset

The frequent revisits of satellite imaging constellations may accelerate existing efforts to quickly update road network and routing information. A fully automated approach to road network extraction and travel time estimation from satellite imagery therefore warrants investigation. Such an investigation requires a large and well-labeled dataset, and this is exactly what the SpaceNet dataset aims to accomplish.

SpaceNet now incorporates 10 cities, with multiple imagery types (e.g. panchromatic, multispectral, RGB) and attendant hand-labeled building footprints and road centerlines. The most recent SpaceNet 5 dataset adds 4 cities to the SpaceNet corpus, with road centerline and metadata…



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