  Street maps are a crucial data source that help to inform a wide range of
decisions, from navigating a city to disaster relief and urban planning.
However, in many parts of the world, street maps are incomplete or lag behind
new construction. Editing maps today involves a tedious process of manually
tracing and annotating roads, buildings, and other map features.
  Over the past decade, many automatic map inference systems have been proposed
to automatically extract street map data from satellite imagery, aerial
imagery, and GPS trajectory datasets. However, automatic map inference has
failed to gain traction in practice due to two key limitations: high error
rates (low precision), which manifest in noisy inference outputs, and a lack of
end-to-end system design to leverage inferred data to update existing street
maps.
  At MIT and QCRI, we have developed a number of algorithms and approaches to
address these challenges, which we combined into a new system we call Mapster.
Mapster is a human-in-the-loop street map editing system that incorporates
three components to robustly accelerate the mapping process over traditional
tools and workflows: high-precision automatic map inference, data refinement,
and machine-assisted map editing.
  Through an evaluation on a large-scale dataset including satellite imagery,
GPS trajectories, and ground-truth map data in forty cities, we show that
Mapster makes automation practical for map editing, and enables the curation of
map datasets that are more complete and up-to-date at less cost.

Computer Vision and Pattern Recognition