  Accurately maintaining digital street maps is labor-intensive. To address
this challenge, much work has studied automatically processing geospatial data
sources such as GPS trajectories and satellite images to reduce the cost of
maintaining digital maps. An end-to-end map update system would first process
geospatial data sources to extract insights, and second leverage those insights
to update and improve the map. However, prior work largely focuses on the first
step of this pipeline: these map extraction methods infer road networks from
scratch given geospatial data sources (in effect creating entirely new maps),
but do not address the second step of leveraging this extracted information to
update the existing digital map data. In this paper, we first explain why
current map extraction techniques yield low accuracy when extended to update
existing maps. We then propose a novel method that leverages the progression of
satellite imagery over time to substantially improve accuracy. Our approach
first compares satellite images captured at different times to identify
portions of the physical road network that have visibly changed, and then
updates the existing map accordingly. We show that our change-based approach
reduces map update error rates four-fold.
