Diary Entries in English

Recent diary entries

Opensnowmap style reload

Posted by yvecai on 12 November 2018 in English (English)

I've figured out recently that base map (topo) renders underground water pipes like plain rivers. It will need a database reload to fix this. Certainly I can correct other glitches at the same time, so have you spotted or can you find something else really wrong with this style? I'm mainly interested in mountainous places, of course, don't expect fixes in Manhattan subway lines ;-) The style is derived from OSM-bright. Thanks in advance, Yves

So how does the Facebook's AI Assisted Road Import Process work?

Posted by Jeff Underwood on 12 November 2018 in English (English)

So how does the Facebook's AI Assisted Road Import Process work?

Although the team has shared the process in depth at various conferences and discussion post and the wiki, we have gotten feedback that we could clear up some of the processes by providing more details. So here it is.

At Facebook, we use our ML (machine learned) data as a starting point but then completely re-edit, fill in the gaps, and correct errors with human editing. Machine learning can do amazing things, but even the best predicted area will always have issues that need a human to fix. An untouched ML file will often have disconnected ways, gaps in the network, road type inconsistencies, and geometry issues. The job of our human editors is to use this ML data as a base and build fully fleshed out, high quality data.

The Machine Learned XML file

This is the base file we work with. Our engineers take a snapshot of OSM data at the time of XML creation. They then merge our predicted roads onto this OSM data to produce the Machine XML file. From this starting point, the editor will cleanup and expand upon the ML data. A limitation that will be immediately apparent to some, is that by necessity, our ML data is merged with a snapshot rather than live OSM. This means that if we take too long to map it, the data could gain conflicts or someone could map out our predicted area. Luckily, our FB iD has some features that reduce the impact of these problems, which I will go into later on below. image_1 A typical Machine file. There are some breaks in generated roads and lints in pink.

Creating a Machine File image_21 A snapshot of OSM data is taken. Here you can see the town has been mostly digitized but many roads to the South are missing.

image_20 Generate a road prediction for the area. All the roads are predicted, even those that exist in OSM already.

image_22 Conflate the machine learned data with live OSM. Roads that already exist will be dropped from the ML output.

image_19 Final Machine File result. predicted roads that do not exist in OSM already are created. Predictions outside of the task bounds are included in a different XML.


Fresh out of the oven, our ML XMLs will typically contain several automatically applied validation checks that must be resolved and removed before our version of iD will even allow the file to uploaded. This sort of automated error flagging is called linting in software development.

Let's go into excruciating detail!

lint_disconnected When a road or cluster of roads does not connect to the greater road network they will get tagged with this lint. Once one of these roads is connected to the network, the tag is automatically removed from all the ways. Our policy is to either connect or delete as we don't want to create a bunch of roads divorced from the rest of the map.

lint_disconnected A typical lint_disconnected

lint_connectExtend This lint is essentially the "Way end node near other highway" check in JOSM. If one of our generated roads ends near another highway the machine will generate this to let our editors know that a connection might be possible here. These missed connections are often from obscuring trees or buildings and are typically confirmable with alternate imagery. Of course, some times the lack of connection is appropriate and in these instances, the way is evaluated and the tag just deleted.

lint_connectextend A typical lint_connectExtend

lint_crossWaterway To speed up the editing process, the machine file will automatically split and add a bridge tag when one of our roads crosses a waterway. This lint tag prompts the editor to first check the validity of the water feature as many are poorly digitized or difficult to see in satellite, then to adjust the automatically created bridge segment to the actual size of the bridge in imagery.

lint_crosswaterway a typical lint_crossWaterway

SplitPoints and lint_autoconnects When the ML data is being gridded up into tasks, generated roads that travel outside the bounds of a task are automatically split at a node and a tag is applied to tell the editor not to move this particular point. When it comes time to publish the data, if the other half of the split point road has already been uploaded in the neighbor task, our iD will automatically load the adjacent road and connect the two halves of the road together. When this happens, a lint_autoconnect tag is applied which cues the editor to check the connection for correctness and the highway types for consistency.

image_2 The red points at the end of the streets are SplitPoints. Our roads should automatically connect between tasks where these are present.

lint_autoconnect Example of an autoconnect. Notice that the adjoining road is also one of ours.


Our engineers have created a more advanced version of iD that has been fully customized for our purposes. The general design philosophy was to add some of the great features JOSM has while retaining the simplicity of iD.

image_3 FB iD

Load Live Data

Despite our XMLs being based on OSM data from the date we generated them, our iD has a feature to automatically pull the latest OSM data and update any preexisting road in the task area. Using this, we avoid experiencing almost all conflicts as our data is fresh each time we open our task area. If any duplicates pop up, like when a road we predicted has been digitized by someone else, these will be loaded and typically caught by our validator with a crossing ways warning.

image_4 The loading live data dialogue


Our version of iD has a number of handy enhancements built in to make it a much more powerful tool than the official version. Most useful to the community at large is our built in validator. This has many of the same checks that JOSM uses, such as overlapping ways and duplicate nodes, and helps prevent errors or poor quality data from ever reaching the live map. Like JOSM, we show a list of validation checks categorized into errors and warnings with errors blocking data upload until they are resolved. Our lint checks are also picked up by this validator as errors and also will also block submission.

image_5 Users can select and cycle through the errors using the arrow buttons. The way involved is highlighted and the error type is displayed in the corner


In our iD we display all of our ML roads in green, or pink if there is a lint tag. We restyled most road types from default iD to make them distinct in various ways since color alone was no longer an option.

image_6 The style sheet for ML data.

image_7 The style sheet for pre existing data.

Additionally, we developed two style toggles to aid our editing process. The first will turn roads bright blue if they have been touched by editor. This helps the editor track their progress as they work the task.

g_key Example of “edited roads” style

The second will randomly color our roads in order to show ways that could be better merged or split. Affectionally known as rainbow roads.

rainbow_roads Example of random color or “rainbow roads“ styles.

Also in the realm of styling, we created a grid overlay to break the task down into smaller pieces to also aid in editing. This can be set in whatever size is most comfortable for the user or particular task.

grid Showcase of the various grid options

Our ML prediction layer is also available as a toggle-able overlay. This can be handy while reviewing to ensure that the editor did not trim out any data we consider high value. You might also notice that we can fully turn off the data layer, rather than just wireframes. Another really handy feature.

ml_layer Example showing the ML layer


Getting from the Machine file to data onto the map is a multistep process for us. Everything must go through three stages of editing before it makes it gets uploaded and afterward we do an additional round of cleanup. image_8 The workflow stages

Editing Workflow

An editor will typically start in a corner and work their way along a task, going through ML roads one by one. They will check the generated highway types for correctness and change as necessary, improve the geometry or connections of the way, and, of course, evaluate if the road is even worth keeping. Marginal tracks are often discarded due to low impact for the amount of work needed to complete them.

image_9 A task before editing

image_10 and after

As a team, we are very cautious when changing community data as people are understandably protective of their hard work. People on the ground will have more context than we ever could as well. In iD, we actually disable deleting or splitting preexisting roads completely. When we do have to make alterations, an editor will leave a lint_fixme explaining the needed fix, typically road type changes. The reviewer will then evaluate if the change is warranted and make it themselves.

image_11 Community road with a lint_fixme saying it should be upgraded to unclassified

Once the geometry, tagging, and visible lints are cleaned up, the editor will run through the errors and warning on the validation panel for anything they might have missed.

validation_panle A small lint_disconnected was missed in the initial editing but immediately obvious with the panel

Lastly, an editor will zoom out and take a look at the highway tagging structure. Does it look correct? Does it follow a logical hierarchy? Only once they are satisfied do they click save to FB backend and mark the task as done. At this point, no data has been added to the live map.

image_12 Marking done on our internal OSM tasking manager

Reviewing Workflow

Reviewers follow a very similar procedure to editors. They also work grid by grid and check all the work of the editor. Additionally, they will make any changes needed to pre existing data that the editor requested.

review_fixmes The editor requested this community road be upgraded to an unclassified. In this case, the reviewer agreed and made the change.

When they do find issues they drop a lint_review tag and leave a note detailing what is wrong. These can be for incorrect tagging, missed or incorrect connections, or any number of number of issues.

lint_review The geometry here is not satisfactory so the reviewer left a lint_review with the note improve geometry

If the reviewer is not satisfied with the quality of work, the task is sent back to the editor for additional editing.

image_13 Sending back in our internal OSM Tasking Manager

If everything looks good, then the task is approved and we're on to publishing.

image_14 Approving in our internal OSM Tasking Manager

Publishing Workflow

Once a task has been approved by a reviewer, it is ready to be published onto OSM. The publisher will go through and cleanup any remaining lint_fixmes and make note of what needs fixing in JOSM after submission. They then click "Publish to OSM", add a changeset comment with our hashtag "#nsroadimport #thailand" and a comment saying what types of roads have been added and noting if any pre existing data has been altered.

image_15 a typical changeset comment

Our engineers have built a more robust conflict handler into iD so when conflicts do arise we can handle them in a painless manner. Rather than simply having to choose between my version or theirs we have the option to keep both sets of edits and intelligently merge the changes.

image_16 The enhanced conflict screen. Keep both is almost always the best choice.

Post-submission Cleanup

After uploading the ML data in iD, the publisher will then open the live OSM data in JOSM to do a final validation check and cleanup the borders of a task area. This part happens in JOSM since it has more robust validation tools than even our FB iD and we want to catch everything we can. We make an effort to combine our ways along task edges and to add consistency to our road tagging. Additionally, if there was any further work that had to be done to preexisting data, such as splitting a community road, we do it during this step.

image_17 A task opened in JOSM. We have a custom map paint style that looks similar to our iD. In addition to the data we overlay the project grid on top so that task bounds are clearly defined.

cleanup Showing the before and after in a typical task in JOSM. Notice the yellow intersections disappearing as roads along the border get merged. In the Southeast corner, two roads are extended to complete them.

Full Project Check

After a project is fully uploaded, we do a final cleanup of the submitted data. We load up an entire project grid in JOSM then download the data in the area. This is very similar to the previous JOSM cleanup step but at a grander scale. Some road type decisions are much easier to make once an entire area is present so this is an important step in making our data cohesive and correct. Again, we check task borders for any connections that need to be merged or were missed and run validation on the entire area.

image_19 An entire project loaded up in JOSM

Rinse and Repeat a Few Thousand Times

And that's how we do it. I hope this helps to make our process clearer. Please feel free to reach out if you want anything further clarified. We're happy to share :)

Glossary of terms

  • Machine learning (ML): a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed. For our purposes, this means teaching a computer to recognize roads from satellite imagery.
  • ML prediction: This is the output of our machine learning. In its raw form its a black and white image showing where the machine thinks roads exist. Our engineers run scripts to process this and turn it into our Machine XML files.
  • ML roads: OSM roads created from the ML prediction. AKA predicted roads
  • Linting: The process of running a program that will analyse code for potential errors AKA engineering speak for validation
  • XML: The file format OSM data is stored in. Often times saved with the .OSM extension though.
  • Machine Learned (ML) XML: a snapshot of OSM data that we merge our ML predicted roads with
  • JOSM: An advanced OpenStreetMap editing application that we use for validation.
  • FB iD: More accessible OpenStreetMap editing tool (that we have customized and use primarily).
  • OSM Tasking Manager: a tool for gridding out areas for mapping. Our internal version is based on OSMTM 2 while the current HOT OSMTM is the newer version 3
  • Community data: OSM data created by someone outside of Facebook .

*Helpful links *

Import discussion

Import Wiki

Thailand Discussions

Indonesia Collaboration with Local OSM Community and HOT

OSM community – you're great!

Posted by tyr_asd on 11 November 2018 in English (English)

Normally, I blog here more about technical topics, but today I would like to take the opportunity to honor three small parts of the OSM community, who I happened to have the chance to get to know over the years that I've been active in mapping. All of these groups deserve a shout-out for their great work they do on OpenStreetMap month after month.

  • The OSM community in Graz (Austria) managed to map their city in great detail and keeps it complete and up to date. It was a pleasure to be a part of the monthly meetups back when I studied there. I remember many fruitful discussions over good food an a nice beer or two. Keep up the good work, guys!
  • Geofabrik is a company from Karlsruhe (Germany) which provides OSM related services since many years, some of which are available for free to the OSM community. Thank you for maintaining your OSM data extract download service, and for hosting your iconic hack weekends. You're awesome!
  • The disastermappers heidelberg are a group of students of Heidelberg University who regularly organize mapathons and workshops, and try to raise awareness of the benefits OpenStreetMap data towards a wide audience. Don't stop educating the mappers of tomorrow!

Do you also know groups or individuals that do great work in OSM? Let them know! :)

Update of the P-1 and P-25

Posted by apm-wa on 11 November 2018 in English (English)

Much more of the P-1 national highway in Turkmenistan is now a dual carriageway divided highway, so the last couple of evenings I've been updating it based on GPS traces collected and marker tags inserted via MAPS.ME during travel on November 8. It's done, but there will be more to do over time. Pavers were working, and the right of way of the new parallel lane, long neglected, is back in play on much of the P-1 route. All bridges appear to be up, so it's now a matter of bulldozing, grading, graveling, and paving.

Special bonus: We found a new grocery store near our house in Ashgabat today.

Location: Ahal Region, Turkmenistan

Spam is appearing within Changesets

Posted by alexkemp on 10 November 2018 in English (English)

Hard on the heels of my previous Diary (How to report spam) I've discovered spam within a changeset (map edit) for the first time:–


 addr:city  Hà Nội
 addr:district  Thanh Xuân
 addr:housenumber   25
 description    Video Bài Hát Việt Nam
 email  <redacted>
 name   Ngoc Anh
 phone  <redacted>
 start_date 10/11/2018


osm tag translate

Posted by mdmahir on 10 November 2018 in English (English)

I have created a proof-of-concept to translate tags using overpass api, wikidata query service queries. you can write your own queries and get the data from both wikidata and osm.

we can extend this translation for ways,streets, and to so many other properties. [Text]

Adding road names from TIGER using MapRoulette

Posted by mvexel on 9 November 2018 in English (English)

TIGER is the street database from the U.S. Census. An old version of TIGER was used to bootstrap OSM in the U.S. back in 2008. We have come a long way since then (see image below) and OSM is now much better than TIGER in most places.

Image from

New versions of TIGER are released every year and they are still useful. Local governments update it with new roads and street names. If you compare new TIGER data with what's in OSM, you get useful information about where OSM may need improving. If you edit in iD, you get visual cues when roads are missing:

You have to stumble upon them, though. And you only see new roads, not if a street that didn't have a name now has one.

My colleagues in the Telenav map team have run OSM and TIGER 2017 through our Cygnus conflation engine to find those streets in OSM that don't have names yet, but TIGER does have a name. We put them in MapRoulette for a few select areas.

MapRoulette is a micro-task tool that I built originally to clean up the redaction mess after the license change. These days, it is available to anyone who wants help to fix all kinds of small problems in OSM. Scroll through my recent diary entries and you will find blog posts about MapRoulette. Right now I just wanted to walk you through solving these TIGER based name adding challenges.

First I select one of the TIGER Challenges in MapRoulette, for example this one for New Orleans.

The first task I get is this one here:

I have three options on the left, Edit (which takes me to my preferred editor, you can set this in your MapRoulette user profile), Not an Issue for if you can already see on the map that there is no issue, or Skip if for whatever reason you'd rather leave this task to somebody else.

Clicking Edit takes me to my preferred editor, JOSM.

And in fact this street segment has no name. The TIGER roads overlay tells me that this is just a part of Lake Trail Drive. I fix the error and upload.

MapRoulette has already taken care of pre-filling a changeset comment and source, but you're of course welcome to tweak these to your liking.

After the upload completes I am ready to switch to MapRoulette in my browser, click I fixed it and go on to the next task.

Finally, here's a list of all TIGER name challenge we have currently. If you want your city or area included, let me know!

Happy Mapping!

Western Michigan University Mapathon

Posted by Kevin Haynes on 9 November 2018 in English (English)

I am pleased to announce that in celebration of Geography Awareness week AND International Education week. The Western Michigan University geography department has organized a mapathon.

Where: Western Michigan University Classroom E in the Swain Education Library

When: Wednesday, Nov. 14 from 5-10 p.m.

Why: Because points, lines, and polygons will change the world!

Food provided by the Geography Department.

If you are a WMU student, this event is part of the WMU Global Engagement Signature Program.
Link: for more information

Contact me if you have any questions.

Location: Oakwood, Kalamazoo County, Michigan, 49008, USA

The P-25 and the P-21 national highways

Posted by apm-wa on 8 November 2018 in English (English)

Just got back from 2-1/2 days on the road, during which inter alia I traversed the P-25 and part of the P-21 national highways in Turkmenistan. I collected roughly 38,000 ground-level images with Mapillary, but will delete some before uploading due to quality issues. Got good GPS traces, however. Tried using MAPS.ME to collect POIs but there are issues due to its limited universe of categories. I ended up using "Attraction" as a default tag just to mark POIs geographically (including U-turns on newly divided highways and such), and now am plowing through them at my desktop computer to correct them to something resembling reality and OSM guidelines. I hope this will not garner too much opprobrium from the OSM community. I'm cleaning up the mess I've created as quickly as I can, but I did want to collect data as I was traveling!

Professor Okpala-Okaka signs up to join the global community of OSM !!!!!

Posted by Professor Okpala-Okaka on 8 November 2018 in English (English)

Today I have been introduced to the procedures of openstreetmap, HOTtasking manager and missing maps. I discovered errors in mapping of a building in my campus and re-edited it. I llook forward to participating more in this global effort to map the world.

Typhoon Haiyan, five years after

Posted by seav on 8 November 2018 in English (English)

Today, November 8, is the fifth anniversary of Typhoon Haiyan, known locally as Typhoon Yolanda, making landfall in the Philippines. This devastating typhoon, which broke the record for being the strongest landfalling tropical cyclone in history, had a lasting impact on the country and its effects are still affecting Filipinos to this day.

For the OSM Philippines community in particular, Typhoon Haiyan radically transformed what it meant to volunteer our time, effort, and resources to map and provide freely accessible geographic data for the country.

Before, the local OSM community was a pretty small group of hobbyist mappers who enjoyed going outside to map and share information about the places we live, work, and play in. But Haiyan showed us that the work we do in mapping the Philippines can sometimes mean the difference between life and death. Past typhoons and storms like Ketsana (Ondoy), Washi (Sendong), Bopha (Pablo), and the 2013 Bohol earthquake had shown the potential of OSM in applications related to humanitarian efforts and disaster resilience, but Haiyan has thrown that into a painfully clear perspective.

A visualization of the mapping activity from thousands of mappers all over the world before, during, and after Typhoon Haiyan hit the Philippines. (Source)

In the five years since Haiyan, OSM Philippines had fully embraced mapping in the context of disaster resilience. Project NOAH (now UP NOAH and under the UP Resilience Institute) initiated several tasks to map several provinces in the country to provide exposure data. Various international aid agencies and organizations such as the American Red Cross, USAID, the United States Department of State, the Asian Development Bank, together with local groups such as the Philippine Red Cross and the Department of Social Welfare and Development, have organized mapathons and workshops to teach Filipinos how to map and use OSM. At the same time, smaller initiatives like MapAmore, Map the Philippines, and the two PH YouthMappers chapters (FEU Tech and UP Diliman) have also pitched in with resources and events of their own.

Haiyan has transformed OSM in the Philippines. Who knows what the next five years can bring us?

A line graph showing the extreme spike in the growth of OSM data for the Philippines as a result of mapping for Typhoon Haiyan.

P.S. I am aware that there are many mappers who are less than enthusiastic with the involvement of humanitarian people and organizations (like the Humanitarian OpenStreetMap Team and Missing Maps) in OpenStreetMap, but as a mapper from a country that is the the third most vulnerable to natural disasters according to the World Risk Index, I am most definitely not one of them.

Mapping the New Genting Highlands

Posted by Reinhart Previano on 7 November 2018 in English (English)

Major Edits in Resorts World Genting, Malaysia

Today I am going to announce a large change to the OpenStreetMap database. What is it? The complete removal of the old Genting Highlands Theme Park assets.

If you are using Bing, MapBox, or DigitalGlobe (Premium) satellite imagery, you are still seeing the old face of Genting theme park.

Image 1

However, if you use the standard DigitalGlobe imagery, you will see that everything has changed since 2015, the last time I visited this place.

Image 2

I have to admit that adding and editing existing buildings and roads here are difficult. I ran into issues such as the traffic flow for vehicles exiting from the First World Hotel lobby. In making these changes, I relied on some sources:

  • "Genting Highlands June 2018 and September 2018 Progress Update" videos by SouthernCorridorMalaysia on YouTube
  • The indoor directory of Genting SkyAvenue, which can found from the official website here
  • The concept art of 20th Century Fox World Malaysia


Before the start of this project I compared the existing map of Genting Highlands on Google Maps and HERE WeGo.

Here are some notes after adding the changesets.

  • HERE WeGo is still showing the former First World underground links, which also proven the existence of underground tunnels connected to roads near to the Avenue of Stars.
  • These changesets are primarily based on DigitalGlobe Standard imagery, where Google Maps' interpretation of Genting is based from. Hence, the new buildings (especially the SkyAvenue) might look similar to GMaps, even though they are directly traced against the imagery source.
  • The new First World Plaza exit routes have been changed. Now it follows the former Outdoor Theme Park road located at the lakeside as shown in an older satellite imagery (see comments), to the Arena of Stars underground tunnel shown in HERE.
  • There is a distortion in the satellite imagery around SkyCasino, which GMaps weren't aware of and mapped the distorted building. The two YouTube videos, as well as the official SkyAvenue directory above prove the correct shape of the building.

What Has Changed in Genting?

Here are some highlights on major changes in Genting Highlands:

  1. Closure of the old Genting Outdoor Theme Park [1]
  2. Closure of the old First World Indoor Theme Park [2]
  3. Renovation of the old Theme Park Hotel, which later changed its name to "Hotel On The Park" then reverted to its former name [2]
  4. Construction and opening of Awana SkyCentral [2]
  5. Construction and opening of SkyAvenue [1]
  6. Construction and opening of Awana SkyWay, connecting Awana SkyCentral to SkyAvenue via Chin Swee Temple [2][3]
  7. Construction and opening of Grand Ion Delemen Hotel [2]
  8. Construction of the 20th Fox World Malaysia (Delayed) [1]
  9. Construction and opening of Skytropolis, the new First World Indoor Theme Park [3]

[1] Shown on changesets mentioned in this article [2] Already added/changed/deleted prior to this article [3] Might have some mapping issues and/or planned to be edited soon

What Has Been Changed in OpenStreetMap?

A lot: see

Added "Genting SkyAvenue" and "Genting SkyCasino"

Note: The prefix word "Genting" is not neccessary, while this could ease people for searching this new shopping mall in Genting.

Genting SkyAvenue, or simply "SkyAvenue", is a new shopping mall located at part of the former Outdoor Theme Park area. This mall, stretching from the First World Plaza to the Avenue of Stars, hosted many tenants that were previously available at the First World Plaza (FWP). Most of the FWP areas, such as the "Genting Walk", has been closed for the opening of the new "Skytropolis" theme park.

Removed two "lakes" in the former Outdoor Theme Park area.

Image 3, from Wikimedia Commons These "lakes" were built for water attractions on the theme park. Now they have been closed for the construction of the 20th Century Fox World Malaysia.

However, the Outdoor Theme Park roads that surrounds the larger lake has been added as the current exit path of First World Plaza (see comments)

Modified First World Hotel lobby exit paths

Initially, I thought that vehicles exiting from the First World Hotel are guided through the new tunnels at SkyAvenue to the gate where the First World Bus Terminal was built. After consulting with the SkyAvenue indoor directory, I agreed to modify the path to exit at the Arena of Stars.

There are future tenants opening at the Ground Level of SkyAvenue, which does not indicate any vehicle routes. However, the new elevated track is built near the former Bus Terminal, which directs the vehicles to the multi-storey parking area at SkyAvenue.

New Paths at 20th Century Fox World Malaysia

Here, I would also like to give a head start of the mapping progress of the future outdoor theme park.

At the first steps I added some paths that are most likely to be part of the main roads in this theme park, supported by the early concept images. I assigned these paths with highway:road before the theme park is finalized to the public.

At the end, here are a list of changesets for you to discover: #64266771, #64269329, #64269852, #64269863, #64270973, #64279712 and #64280005

Location: Genting Highlands, Pahang, 69000, Malaysia

MapRoulette Insider - Creating a Challenge

Posted by mvexel on 6 November 2018 in English (English)

In this post, part of the MapRoulette Insider series, I will show you how to create a MapRoulette Challenge yourself.

The first thing you need is a Challenge idea. Good challenges have tasks that:

  • Are easy to solve (typically less than one minute)
  • Do not require local knowledge
  • Involve only one or two OSM objects

My example challenge meets those criteria. I ask mappers to review motorway_junction nodes in the United States that have name tags. This is uncommon in the U.S. Often mappers will add a name tag that has the destination information on it, so the information renders on the map:

This Overpass query selects all these nodes.

area[name="United States of America"]->.a; node["highway"="motorway_junction"]["name"](area.a); out meta;

With that query in hand, I can go to MapRoulette and click 'Create' at the top. This takes you to your MapRoulette Projects page. By default you will only have one Project. Click on its name to select it as the home for your new Challenge. At the top of the Project page, you will find the 'Add Challenge' link, click that to start the wizard to add a new Challenge.

The Wizard has 4 pages, but only the first two require your full attention :)

On the first page you enter the title, description and instruction for your challenge, as well as some metadata that will be used to help others find it and identify edits made by mappers using the Challenge:

  • Visible Whether the Challenge will be listed. If no, you can still share the Challenge URL and it will work.
  • Name A descriptive name for the challenge. I used Review named motorway junctions in USA. A good title describes what the challenge is about is a few words.
  • Description Some text to describe in more words what the challenge is about. This field supports markdown and appears in the expanded challenge information when mappers browse for interesting challenges:

I used Motorway junction nodes are not commonly tagged with name. This challenge asks mappers to review those nodes, removing the name where it's not applicable.

  • Blurb Feel free to skip this. An even shorter description of what the challenge is about. I used Review named motorway junction nodes
  • Instruction This is perhaps the most important text. Here you tell mappers exactly what the task is you want them to perform. Be specific and use links (this field supports markdown as well) to the OSM wiki or other external sources where needed. This text is shown next to the map of the task location. I used This motorway_junction node has a name tag. This is uncommon, see the OSM wiki for details. If this name tag does not represent a 'name of the junction or interchange', the name tag should be removed. If you're not sure, you can skip the task.. Note how I use markdown features to make the instructions easy to read and comprehensive.
  • Changeset Description This field will be used to pre-fill the changeset description in the editor. I used reviewing named motorway junctions. A #maproulette hashtag will be added unless you change that default setting below.
  • Changeset Source This field will be used to pre-fill the changeset source field in the editor. If your task involves using any external data, you should list it here. I used maproulette;overpass.
  • Difficulty You can leave this at 'Normal' unless your tasks are particularly easy or hard. We may change this system in the near future..
  • Category This helps the user find challenges they are interested in. There is a dropdown menu on the main Challenge list that lets users narrow down the list by category. For this challenge I used Roads / Pedestrian / Cycleways.
  • Keywords These also help users find interesting challenges. They are used in the free text search field. I used junction, name, motorway and exit.

When all this is filled out, you can proceed to screen 2, where you supply the source data for the tasks. This can be a GeoJSON file or URL, or an Overpass query. If you use an Overpass query, make sure you don't use any Overpass Turbo specific language such as {{geocodeArea:...}}. Test your query in Overpass Turbo first.

The final two screens let you add rules to prioritize certain tasks over others based on OSM tags, and let you define some view related settings for your challenge. The defaults are sensible but please do review them and tweak as needed. Make sure that the query only returns the nodes or ways that you want mappers to review, and ideally the number of objects returned should be no more than a couple thousand.

When you click 'Finish', MapRoulette will query Overpass in the background and populate your challenge. Depending on how heavy the Overpass query is, this can take a few seconds to a couple of minutes. When it finishes, you will see a map with your tasks, a complete list of tasks and some more challenge information.

You can now click 'Start' on the top to go directly to your challenge and try it out!

2018 Laos dam collapse imagery

Posted by ff5722 on 6 November 2018 in English (English)

The Sentinel-2 satellite finally captured a cloud free coverage of the 2018 Laos Dam Collapse site. Over a distance of more than 50km, everything near the river has been washed away, trees being stripped bare from the river banks.

Comparing it to the river pre-disaster shows how much energy was in the water:

The map below shows what happened, one of the secondary dams overflowed, causing the reservoir to drain into another river.


Location: Sanamxay District, Attapeu, Laos

Make maps

Posted by captaininler on 5 November 2018 in English (English)


Posted by rachelezell71 on 5 November 2018 in English (English)

Bad storms coming tonight. Trying to update the radar maps for everyone.

Location: Glasgow, Barren County, Kentucky, USA

Finding Missing Roads in the Philippines

Posted by GOwin on 5 November 2018 in English (English)

... or maybe just Nemo then. ;)

Road data are essential for most maps, whether they're used for tourism, navigation, or business - but especially critical in emergency response. Our project goal is to validate and map the road network that connect settlements and residential areas for the country, and make this open data available to all through OpenStreetMap.

Together with contributors from the local community, including the enthusiastic volunteers of the PUPSJ CWTS++ program, we've completed or made head-way in some regions in the recent past.

Utilzing the ImproveOSM tools, contributors were able to validate, identify, and map, the potential missing roads in the countyr some months back, with technical and logistical support from Kaart.

image The Philippines, compared to neighboring countries visualized from ImproveOSM's iD editor.

We've recently updated our GIS analysis of available data, and refreshed our Philippine tasks :

We're also introducing a new validation approach for spotting potential missing roads and make them easier for beginners and experienced mappers alike, regardless whether they're using iD or JOSM.


With this, we hope to continue improving local open map data, working along-side contributors from various local communities.

image On average, Philippine road data increase is usually < 2%. Between July and October, we contributed to bumping this up by 3%

Start mapping the missing roads of your favorite neighborhood, or your home town - or surprise yourself by allowing the tasking manager to pick a task for you randomly. Head over to the HOT Tasking Manager, or use this short link to jump to our project:

P.S. Last screenie is from the awesome Map Metrics tool of the ImproveOSM project:

Location: Pinyahan, East Triangle, District IV, Quezon City, Metro Manila, 1100, Philippines

More moving values in to keys madness

Posted by SimonPoole on 4 November 2018 in English (English)

I just received a request to add a preset for amenity=language_school to my fork of the JOSM presets (that you should be using instead of the original :-)) Nothing to said against that, it is a reasonably popular feature with nearly a 1'000 uses.

Naturally a preset that is essentially just a stub isn't really helpful, so as always I checked what additional properties should be added, and tagging the taught languages is an obvious attribute that is interesting.

Much to my dismay it seems as if in early 2016 it was infected with the "move values to key" plague. Instead of having two keys, lets say




containing a list of the languages, we now have more than two hundred potential keys for an in principle, simple attribute, making both data consumption, editing and creating a preset more than just difficult (as the keys can have at least three values plus unset, they can't even be modelled with a checkbox in a preset).

At the time somebody further thought it was a good idea to retag existing language tags to the broken schema:

Insomnia, and Cleaning Up Gas Stations

Posted by apm-wa on 4 November 2018 in English (English)

Insomnia hit last night for the first time in years. With nothing better to do I cleaned up gas stations in Turkmenistan, identified stations without numbers and placed FIXME tags on them, added data where I had collected it but not got around to updating the map. Is there some way of getting overpass turbo to count stations for me? When Ann and I started this project in 2015 there were very few gas stations mapped. There seem to be a lot more now, between our work and the work of other mappers here.

Hope to get back on the road next week to do some more exploring.

Using the new OSMCha feature API endpoint

Posted by wille on 4 November 2018 in English (English)

On my last diary post I talked that we have a new features endpoint on our OSMCha API that makes it easy to the communities to add flagged features to OSMCha. Let's check how you can use this new endpoint.

1. Get authorization

The first step is to open an issue on our GitHub repository and tell us what kind of feature modifications you want to flag.

You'll need to deploy some software to monitor the features or you can just write an osm-compare function.

We will review your application and, if you prefer to run a monitoring software by yourself, we will update your user to allow it to post features to OSMCha.

2. Post features

The new endpoint is You must do a POST request with the following content:

{ "osm_id": "4321", "changeset": 1234, "osm_type": "node", "reasons": ["Other reason", "Large building"], "version": 54, "name": "Tall Building", "primary_tags": {"office": "coworking", "building": "yes"}, "note": "details about the suspicion" }

Only the first four fields are mandatory. The other are optional. Any other field you add to the request content will not be saved on the database. Check the list of existing suspicion reasons, if you want to flag features with another reason, just add the name of the new reason inside the reasons field and it will be created when the request is received by the server.

If you need some additional help, open an issue on the osmcha-frontend repository or send an email to the OSMCha mailing list. We will be glad to help you validate more changes in OpenStreetMap!