New Features and Enhancements in Cygnus+

Cygnus is the Telenav Mapping conflation tool. We use it a lot internally to compare approved external data sources with existing OSM data, but there is also a public version. We outlined how it works in an earlier blog post. In this post, I want to highlight some of the newer features in Cygnus. These new features are based on the feedback from our team of Map Analysts, who use the tool in their day to day work.

Discarding Very Short Segments

Cygnus outputs the differences in geometry between existing OSM data and the spatial data that we want to use to improve OSM. Sometimes, when the differences are very tiny, Cygnus used to export very short ways. These are not really meaningful enhancements, and clutter up the result data. Therefore, we implemented a length filter. Ways shorter than a defined length threshold will not be included in the output. Based on experience, we set the default to 5 meters. In the internal (command line) version our team uses, this can be tweaked using a parameter. In the public web version, this is not yet possible. We can consider adding it if there is sufficient demand.

An example of Cygnus in action. It finds an opportunity for improvement (possibly incorrect street name) as well as a false positive (degraded road geometry)

Road Names

When comparing road geometry, Cygnus not only compares geometry, but also road names. An annoying side effect we noticed is that road names are often not exactly the same in OSM as they are in the external data we compare with. This does not mean that the external data is necessarily better. For example, OSM could say that the name of a road is “River Road”, and the external data source could say it is “River Rd”. This is not a meaningful difference, and we would want to exclude those in most cases. So we added a string distance based  threshold in Cygnus to filter out similar strings. It is set to a sensible default which, again, can be tweaked in the command line version we use internally, but not yet in the web version.

Another Cygnus improvement related to road names is to ignore name differences on certain types of ways: roundabouts and service roads. Roundabout ways in OSM do not have names by convention, unless the roundabout itself has a name, so they should generally not be added. Service roads technically can have names in OSM, but it is not common. In external data, they do sometimes have names, but if they do, it usually does not make sense to add them to OSM. Based on our experience, they often have descriptive names like ‘driveway’ or ‘access road’ in the source data.

Using Cygnus

You can use Cygnus yourself by going to and uploading your source data file. You need to do a fair amount of work to prepare the source data: translating the source attributes into valid OSM tags, and converting to OSM PBF. And always remember to consider carefully what you do with the result. Cygnus is not designed to be an automated import tool. Every suggested change should be manually reviewed.

Let us know how you have used, or would like to use Cygnus!


Fire up the editors: ImproveOSM updated with many new things to fix in OSM

Our OSM team continually processes billions of anonymized GPS traces we receive through the Scout app and partners, in order to discover things potentially wrong or missing in OSM. We call this effort ImproveOSM, and it  is a big part of Telenav’s overall mission to keep making OSM even better.

Missing Roads in Northern Brazil. The denser the GPS point cloud, the more trips and the more likely you are helping people get around more accurately!

Our most recent update to ImproveOSM was a particularly big one. In the last month, we added:

  • 133 thousand missing roads tiles
    • Another 75 thousand tiles that are likely parking areas or tracks
    • Another 670 thousand (!) water tiles (see below)
  • 300 thousand suspected turn restrictions with over 50% high confidence

Using ImproveOSM data

Perhaps you have not looked at ImproveOSM data before. It is available through the ImproveOSM web site, which is based on the iD editor. The screenshots on this page are from that web site. If you know how to edit with iD, you will find it easy to work with ImproveOSM data and use it to edit OSM. We wrote a post that goes into more detail a little while ago.

If you prefer JOSM, we have created an ImproveOSM JOSM plugin as well. it works similar to the web site: you choose what ImproveOSM data you want to see (suspected missing roads, suspected wrong one-way roads, or suspected missing turn restrictions, or all of the above!) and the plugin will show you the ImproveOSM data as a separate layer. We also have a blog post about using the JOSM plugin.

Finally, a few interesting / funny examples of ImproveOSM data around the world.

ImproveOSM data points out that a new road alignment is now in use. Aerial imagery and OSM have not been updated yet. This is in northern Sweden.

Here, we stumble upon an undermapped town north of Surat, India. Of course, there are un- and undermapped areas everywhere in the world, but the ImproveOSM data shows that there are people driving around on these streets using a GPS enabled app or vehicle — people who would benefit from better OSM data in their everyday lives. It is not hard to find places like this around the world.

Finally, an animation showing clusters of ‘water’ tiles. This is a side effect of the partner data we process. Since it’s anonymized there is no way to say anything about why these traces exist. Useful for OSM? Perhaps.. Interesting? I think so!

Are you finding interesting, useful, funny or wrong data in ImproveOSM? Let us know! Happy Mapping!


Is OpenStreetMap Big Data ready?

This article was written by Adrian Bona as a draft for a talk at State of the Map US in Boulder, Colorado this past month. The talk did not make it into the program, but the technology lives on as a central part of our OpenStreetMap technology stack here at Telenav. We will continue to deliver weekly Parquet files of OSM data. Adrian has recently moved on from Telenav, but our OSM team is looking forward to hearing from you about this topic! — Martijn

Getting started with OpenStreetMap at large scale (the entire planet) can be painful. A few years ago we were a bit intrigued to see people waiting hours or even days to get a piece of OSM imported in PostgreSQL on huge machines. But we said OK … this is not Big Data.Meanwhile, we started to work on various geo-spatial analyses involving technologies from a Big Data stack, where OSM was used and we were again intrigued as the regular way to handle the OSM data was to run osmosis over the huge PBF planet file and dump some CSV files for various scenarios. Even if this works, it’s sub-optimal, and so we wrote an OSM converter to a big data friendly columnar format called Parquet.The converter is available at, this will make the valuable work of so many OSM contributors easily available for the Big Data world.

How fast?

Less than a minute for romania-latest.osm.pbf and ~3 hours (on a decent laptop with SSD) for the planet-latest.osm.pbf.

Getting started with Apache Spark and OpenStreetMap

The converter mentioned above takes one file and not only converts the data but also splits it in three files, one for each OSM entity type – each file basically represents a collection of structured data (a table). The schemas of the tables are the following:

 |-- id: long
 |-- version: integer
 |-- timestamp: long
 |-- changeset: long
 |-- uid: integer
 |-- user_sid: string
 |-- tags: array
 |    |-- element: struct
 |    |    |-- key: string
 |    |    |-- value: string
 |-- latitude: double
 |-- longitude: double

 |-- id: long
 |-- version: integer
 |-- timestamp: long
 |-- changeset: long
 |-- uid: integer
 |-- user_sid: string
 |-- tags: array
 |    |-- element: struct
 |    |    |-- key: string
 |    |    |-- value: string
 |-- nodes: array
 |    |-- element: struct
 |    |    |-- index: integer
 |    |    |-- nodeId: long

 |-- id: long
 |-- version: integer
 |-- timestamp: long
 |-- changeset: long
 |-- uid: integer
 |-- user_sid: string
 |-- tags: array
 |    |-- element: struct
 |    |    |-- key: string
 |    |    |-- value: string
 |-- members: array
 |    |-- element: struct
 |    |    |-- id: long
 |    |    |-- role: string
 |    |    |-- type: string

Now, loading the data in Apache Spark becomes extremely convenient:

val nodeDF ="romania-latest.osm.pbf.node.parquet")

val wayDF ="romania-latest.osm.pbf.way.parquet")

val relationDF ="romania-latest.osm.pbf.relation.parquet")

From this point on, the Spark world opens and we could either play around with DataFrames or use the beloved SQL that we all know. Lets consider the following task:

For the most active OSM contributors, highlight the distribution of their work over time.

The DataFrames API solution looks like:

val nodeDF = nodeDF
    .withColumn("created_at", ($"timestamp" / 1000).cast(TimestampType))

val top10Users = nodeDF.groupBy("user_sid")
    .map({ case Row(user_sid: String, _) => user_sid })
nodeDF.filter($"user_sid".in(top10Users: _*))
    .groupBy($"user_sid", year($"created_at").as("year"))

The Spark SQL solution looks like:

    year(created_at)) as year,
    count(*) as node_count
    user_sid in (
        select user_sid from (
                count(*) as c 
            group by 
            order by 
                c desc 
            limit 10
group by 
order by 

Both solutions are equivalent, and give the following results:

alt tag

Even if we touched only a tiny piece of OSM, there is nothing to stop us from analyzing and getting valuable insights from it, in scalable way.

If you are curious about more advanced interaction between OpenStreetMap and Apache Spark, take a look at this databricks notebook.

OpenStreetMap Parquet files for the entire planet?

Telenav is happy to announce weekly releases of OpenStreetMap Parquet files for the entire planet at


Find your MapRoulette Challenge

MapRoulette is a fun way to spend a few minutes (or hours…) improving OpenStreetMap. MapRoulette will present you with a random, easy to solve issue in OSM. MapRoulette is organized in ‘Challenges’, groups of tasks that are of the same nature. For example, there is a challenge to add missing crosswalks in various areas in Switzerland, based on analysis of aerial images.

How do you find a challenge you would like to work on? The MapRoulette home page provides a map of all the challenges, but this has some shortcomings. The challenge ‘centers’ are no

t always representative of where the tasks actually are located. It is also hard to search by topic. MapRoulette also has a search bar that you can use to find a challenge by keyword.

I want to work on making it much easier to find

 interesting MapRoulette challenges, and I would like to hear from you how you think that should work. Please add a comment below with your ideas!

In the mean time, I made a page that lists the most popular and newest challenges. It is a bit of a hack so let me know if it stops working 😉

Happy Mapping!


Help fix up TIGER v1 ways

Old, untouched TIGER ways are still abundant in OSM 🙁 and fixing them up seems to be an endless task.


I don’t know why I didn’t do this before, but I finally got around to making a MapRoulette challenge so we can fix them together:

>> Go to the challenge <<

Because the number of old TIGER ways is huge, this challenge covers only a tiny part of the U.S. as you can see here:

Once this part is done, we can reload the challenge with more old TIGER ways.

If you look at the screenshot above, you can also see what the query is that goes into Overpass to create the challenge in the first place. You can easily adapt it to make your own local challenge if you want to start fixing up old TIGER ways with your local mapping friends! (Why not organize a TIGER fixing party? OSM US will pay for pizza!)

If you’re interested in the Overpass details and some ideas for improving it, keep reading. Otherwise, just start fixing! 

Query Overpass for old TIGER

Here is my extremely simplified way to query Overpass for old TIGER ways:

way[highway]["tiger:tlid"](40, -113, 41, -111);
out body geom qt;

It takes the bounding box (40, -113, 41, -111) and searches for ways that have the highway tag as well as the tiger:tlid tag. This query should be a pretty good approximation of a real old TIGER way query, because the tiger:tlid tag is removed automatically when you edit such a way in iD or JOSM. So any way that still has this tag must not have been edited since the import.

This query falls short of a real old TIGER ways query, because the nodes that make up the way may very well have been edited. I am also not 100% sure under which circumstances the editors remove the tiger:tlid and other unnecessary TIGER import tags. It may be safer to look for last edited date or version number. If you have suggestions for improvement, please let me know in the comments.

Happy mapping!


More and Updated Data for ImproveOSM

ImproveOSM has been updated with many new roads. We processed recent  GPS data from a number of data partners with some great results. A total of 30,000 new missing road tiles were added, over 17000 in Indonesia alone.

Aside from the missing roads, we added 67000 potential missing one-way roads that we detected with high confidence. Internal testing revealed only 6% false positives.

We are happy to continue providing OSM mappers with high quality data about missing things in OSM based on billions of GPS traces. Because ImproveOSM is based on actual drives from people using navigation or mapping software in their vehicles, and we apply a pretty high threshold for number of trips and quality of the GPS data, you can be pretty confident that every ImproveOSM feature will lead you to something you can add to OSM. Even if the aerial imagery is poor.

You should see the new data in your ImproveOSM plugin or on the ImproveOSM web site very shortly. Happy mapping and let us know what you mapped using ImproveOSM!


New Version Of OpenStreetCam Introduces Points

Late last week, we released new versions of the OpenStreetCam apps and the web site. While we continue to make the platform faster and more reliable, we also like to keep adding interesting and fun features from time to time! This new release introduces points and levels. Every time you drive, you earn points. Earn enough points and you level up.

We went back in and calculated points for all your existing trips, so why not head to the newly designed leaderboard and see how you stack up against your fellow cammers? You can also see the leaderboard in the app:

We also enabled leaderboards by country on top of the daily, weekly and monthly rankings.

Your profile screen in the app and on the site will show you exactly how many points you have, how many you earned per trip, and what your current level is.

The new profile screen

More points for unexplored roads

So as you are driving around, you will automatically earn points for every picture recorded. But not all pictures earn you equal points! The less explored a road is, the more points you get — up to 10x the points for roads that have no coverage at all yet!

(This made it possible for me to gain 11k points on a 50 minute drive last week: most of the roads had no coverage yet, so I was getting 10x points for most of the way. )

11k points!

You can see which roads are less covered, or not covered at all yet, in the app. Just look for the roads with lighter or no purple OSC overlay:

Darker streets have better coverage, lighter streets need more.

We calculate the quality of coverage by the number of trips that cover the way as well as the age of the existing trips. This way we encourage each other to always have the most recent imagery available for OpenStreetMap.

We hope you enjoy the new features! Please let us know what you think by writing us at


OSMTime in Cluj featuring MapRoulette

OSMTime is a monthly OSM mapping event organized by Telenav colleague Beata Jancso. Telenav hosts the events in the Cluj-Napoca office  and sponsors with pizza. Usually Bea chooses a theme and sometimes there will also be a speaker with an interesting OSM related topic.

While visiting the Telenav Romania office in Cluj last week, I was lucky to also catch an OSMTime event. The theme of the evening was ‘Mapping Roundabouts using MapRoulette’. Being the person behind MapRoulette, Bea asked me to do a quick introduction. Colleague Bogdan Gliga also presented the metodology he used to detect missing roundabouts from massive amounts of probe data. (He wrote about that topic here as well.)

OSMTime Cluj with Bogdan presenting
OSMTime Cluj with Bogdan presenting

After the presentations and pizza, the 25 or so mappers logged on to MapRoulette to start with the new Missing Roundabouts challenge. Most people had not used MapRoulette before, so I was glad that everyone was getting the hang of it quickly. Most of the problems and questions were not about MapRoulette but about what is a roundabout exactly, and what is the difference between a roundabout and a mini_roundabout and a traffic_circle. (The OSM wiki helps out a little here.)

At the end of the evening, the mappers in the room already made a good dent in the challenge, which has more than 4500 tasks total.

I had a great time, thanks to Bea for organizing the OSMTime events every month and spreading the word. If you are in the Cluj-Napoca area, you may want to subscribe to the OSMTime meetup so you know when the next one takes place. Or look for an OpenStreetMap meetup in your area and meet local mappers!


Collaboration brings nearly 1 million missing roads to ImproveOSM

If you go to ImproveOSM today, you will notice that it looks a lot different. No, we are not talking about the recent change to a completely iD-based editing environment, although that was pretty neat too J. We are talking about the massive increase in Missing Road tiles worldwide!

Missing roads everywhere!
Missing roads everywhere!

We added more than 800 thousand new road tiles to ImproveOSM all over the world. The anonymous GPS traces are sourced from INRIX, a company that provides traffic and connected car services. We are extremely excited to have such a huge boost to ImproveOSM and to OSM itself!

If you haven’t tried ImproveOSM recently, why not head over to right now and explore the millions of missing roads, one-way streets and turn restrictions detected from big data analysis on anonymous GPS traces from drivers all over the world?

You can read more about the collaboration with INRIX in the joint press release.


OpenStreetView is now OpenStreetCam

This summer, we launched OpenStreetView and received great response both from the OpenStreetMap community and the press.

After only 4 months, you have already contributed almost 12 million images covering 322 thousand kilometers. We have released open source apps, upload and OpenStreetMap editing tools, and are working on many improvements aimed at improving OSM faster than is possible now.

As part of our fast growing public profile, we have also attracted the attention of Google Inc, who holds the ‘Street view’ trademark. They are really interested in OpenStreetView but also expressed concerns about the name creating confusion. Obviously to us this confusion does not exist, but after considering the pros and cons carefully, we decided to change the name.

From now on, OpenStreetView will be known as OpenStreetCam. 


Aside from the name, nothing changes. In fact, we will be launching some pretty cool new features and improvements very soon, so please stay tuned for that. If you have not tried OpenStreetCam yet, why not download the free and open apps for Android or iOS, explore the coverage or start editing with OSC in OpenStreetMap?

Happy OpenStreetCamming!