“Sliding” datasets together for more automated map tracing

Paul Mach, Strava Inc
Thursday 15:00 - 15:25
Session 3, Track 1, Slot 1

Importing new/updated geometry into large dataset like Open Street Map is tricky business. Features represented in both need to be detected and merged. Often times editors are asked to completely “retrace” over updated maps as automated methods are unreliable.

While a 100% accurate merge is impossible, it is possible to auto create a best guess and let the user refine from there, eliminating as many manual, tedious steps as possible.

Slide is a tool designed to solve this problem and works by iteratively refining roads, trails and other complex geometries to match another dataset, where the features are correctly mapped. In a single click one geometry is “slided” to the other, eliminating hundreds of tedious clicks.

The form of the new dataset is flexible. It could be an updated representation of roads such as the new TIGER database, a scanned historical paper map, or a large collection of GPS data points like the 250+ billion made available by Strava, a fitness tracking website.

Overall, Slide is designed to leverage what we already know, collected in various datasets, to speed map tracing. Map editors should be focusing on higher level challenges and not just retracing over another dataset.