This paper describes an off-line (i.e. pre-navigation) methodology for machines/robots to identify zebra crossings and their respective orientations within pedestrian environments, for the purpose of identifying street crossing ability. Not knowing crossing ability beforehand can prevent path trajectories from being accurately planned pre-navigation. As such, we propose a methodology that sources information from internet 2D maps to identify the locations of pedestrian street crossings. This information is comprised of road networks and satellite imagery of street intersections, from which the locations/orientations of zebra-pattern crossings can be identified by means of trained neural networks and proposed verification algorithms. The methodology demonstrated good capability in detecting and mapping street crossings’ locations, while also showing good results in verifying them against falsely detected objects in satellite imagery. Orientation estimation of zebra-pattern crossings, using a proposed line-scanning algorithm, was found to be within an error range of 4∘ on a limited test set.