  Car-focused navigation services are based on turns and distances of named
streets, whereas navigation instructions naturally used by humans are centered
around physical objects called landmarks. We present a neural model that takes
OpenStreetMap representations as input and learns to generate navigation
instructions that contain visible and salient landmarks from human natural
language instructions. Routes on the map are encoded in a location- and
rotation-invariant graph representation that is decoded into natural language
instructions. Our work is based on a novel dataset of 7,672 crowd-sourced
instances that have been verified by human navigation in Street View. Our
evaluation shows that the navigation instructions generated by our system have
similar properties as human-generated instructions, and lead to successful
human navigation in Street View.
