GPS technology is growing fast along with the mapping industry. There are a lot of navigation apps on the market, trying to step forward using different technologies and map data. As you know, behind all its routing processes and algorithms, a good routing application must have a good quality map.
Our team, Telenav, is developing an Open-Street-Map (OSM) based app, called Scout. Why Open-Street-Map? In my opinion OSM is a good choice because it’s an open, fast growing and well built-up community map. With its great involvement and commitment, the community is constantly trying to keep all the map features from OSM up to date.
For the purpose mentioned above, Telenav decided to improve the quality of the OSM map features, used for navigation, in Phoenix, Arizona.
From September until now, the whole project workflow was realized in 3 main steps:
Step #1. Research and get in touch with local community (research on local open source data, keep in touch with local OSM users, official traffic signs and driving rules, research on HOV and toll roads, roads under construction, other specific road features of Arizona State).
Step #2. Build-up process:
- Road geometry, Road Name, One Way, Gates and Other Geometric Feature (turning circles, turning loops, etc.)
- Turn Restrictions and Traffic Lights
- Lane and Turn Lane Info, including HOV Lanes and Toll Roads
- Speed Limit Editing
Step #3. Quality Assurance (QA for every feature edited by our team in OSM, some road name special issues, repairing broken relations, final edits and corrections based on Improve OSM plug-in, Osmose and Keep Right errors, tile-by-tile verification).
The first step was a very important one because the team had to get in touch with the local community and mapping rules. Also, we researched other open source and free of charge data beside the already well-known sources used by OSM users (TIGER Roads, Bing, Digital Globe, Open-Street-Cam, Mapillary). The under-construction roads found in this step were permanently monitored, to be edited later.
The second step was the most time consuming and it included the editing and reviewing of all the most important navigable OSM features, starting from motorway, trunk, primary, secondary, tertiary, residential and service ways. The whole editing process lasted for about 3 moths and the workflow was mainly based on a tile-by-tile method. We also edited based on way category or route reference. During this period, we helped the community to increase the OSM quality as you can see below:
The third step was the last but not the least important. Because we wanted to have good quality features in OSM, we had to make a closure check on our edits, already made in the build-up process and where it was necessary, to fill the gaps. So, we used different QA procedures, queries, other plug-ins, error identifiers and even tile by tile verification.
For the queries based QA, we ran some predefined scripts based on OSM datasets, using pgAdmin and PostgreSQL. The queries were aimed mainly at finding:
- Wrong one-way direction
- Wrong number of lanes
- Untagged roads
- Long relation members
- Turn Restrictions with unusual number of members
- Ramp has name or reference number
- Similar names and destinations
- Detect possible roundabouts, etc.
The main JOSM plug-in used to complete the missing roads was Telenav ImproveOSM (also available on https://improveosm.org). The plug-in focuses especially on missing roads, one ways and turn restrictions.
Doing the QA, we had an important overview of all our edits, especially of our wrong ones. In this step, we resolved also a name tag issue we’d came across during step #2. Step #3 represents confidence of a good quality editing process for the Telenav team.
When we needed a double check of our work, the community was a good help, giving us important feedback. For the next months, we’ll surely keep in touch continuously with the local OSM users, looking forward for their feedback to keep up a good maintenance work. The top editing users were the most helpful users, giving us precious and pertinent feedbacks.
In the GIF below you can see an evolution of Telenav mapping team edits during the last moths, in Phoenix, Arizona. Darker colors indicate high density and brighter colors low density.