  In the recent years a number of novel, automatic map-inference techniques
have been proposed, which derive road-network from a cohort of GPS traces
collected by a fleet of vehicles. In spite of considerable attention, these
maps are imperfect in many ways: they create an abundance of spurious
connections, have poor coverage, and are visually confusing. Hence, commercial
and crowd-sourced mapping services heavily use human annotation to minimize the
mapping errors. Consequently, their response to changes in the road network is
inevitably slow. In this paper we describe \mapfuse, a system which fuses a
human-annotated map (e.g., OpenStreetMap) with any automatically inferred map,
thus effectively enabling quick map updates. In addition to new road creation,
we study in depth road closure, which have not been examined in the past. By
leveraging solid, human-annotated maps with minor corrections, we derive maps
which minimize the trajectory matching errors due to both road network change
and imperfect map inference of fully-automatic approaches.
