The idea is pretty cool. It downloads regional data from Geofabrik directly in the browser and formats the data into Parquet format, then lets you ask the AI to query it using DuckDB.
The Dakota/Lakota language has been a written language for only the most recent chapters of its history.
Dakota (instead called Lakota by speakers of that dialect) is the indigenous language of the Oc̣eṭi Ṡakowiƞ (Seven Council Fires) people of North America, consisting of the Bdewaḳaƞṭuƞwaƞ, Waḣpeḳute, Waḣpeṭuƞwaƞ, Sisiṭuƞwaƞ, Ihaƞkṭuƞwaƞ, Ih…
My mapping on OpenStreetMap includes names in both Dakota and Lakota, with ISO codes dak and lkt respectively. This is an unusual approach compared to a lot of other multilingual mapping, so it’s worth explaining in more detail.
There are about 450,000 open notes on OpenStreetMap. Each one is a message from someone
who saw a problem on the map. But to know what a note needs, you have to open it and read
it, one by one. That takes a lot of time.
who saw a problem on the map. But to know what a note needs, you have to open it and read
it, one by one. That takes a lot of time.
A few weeks ago I wrote here about Wandering Around, a free browser game in which you can walk through any town or city on Earth, built on the fly from OpenStreetMap. Thanks to your comments and feedback, the game has now left beta: version 1.
According to sources (such as this a general rule of thumb for roads within urban areas is 40 for busy traffic roads and 60 for main roads.
So maybe I’ll map in the big capital city today…
Geospatial Action Against Dengue: Empowering Communities Through Open Mapping and Waste Hotspot Identification in Eravur Pattu, Sri Lanka
I have done 3590 Contributions on OSM today and am still working. I think getting detention was a godsent for my brain but a pain for my fingers. I am still working on more as I do not have any school work left to complete for today. I have done I believe at least 500 buildings today, completing 3 full neighborhoods in my county and starting another. Wish me luck, my fellow mappers. I’m in detention until Thursday as well so I think I will be able to get a lot more done depending on how much work my teachers send me on Thursday.
I maintain a map of vending machines built from OpenStreetMap data, part of a site that connects vending operators with venues (link at the end).
Hi everyone! I’m a university student, mapping around campus as part of a class assignment. I’m still learning OSM’s tagging conventions and sourcing norms, so I’d really appreciate any feedback, suggestions, or corrections on my edits and changeset comments. If you review any of my edits, feel free to leave a comment or reply here. Thanks so much for your time and guidance!
New mapper looking for feedback on campus edits
Hi everyone! I’m a university student, mapping around campus as part of a class assignment. I’m still learning OSM’s tagging conventions and sourcing norms, so I’d really appreciate any feedback, suggestions, or corrections on my edits and changeset …OpenStreetMap
The Mystic Link Trail is a conceptual effort to link together various green spaces to form a route from Ward Reservation in Andover, MA, US down to Boston, ending at the wharf where the Constitution is docked.
After mapping I will walk around and slowly put all the house addresses.
Ten years ago, I attended my first State of the Map LATAM in São Paulo, thanks to a travel grant. Until then, for me, OpenStreetMap was just the group we’d formed in Mexico out of the legendary Rancho Electrónico, Mexico City’s hackerspace.
Next Tuesday the OSMLondon pub meet-up will be at The Volunteer pub near Baker Street!
see https://community.openstreetmap.org/t/update-on-my-id-tagging-schema-and-id-grants-completion-of-612-hours-of-work/149730 for overview
Update on my iD tagging schema and iD grants - completion of 612 hours of work
see https://community.openstreetmap.org/t/update-on-my-id-tagging-schema-and-id-grants-completion-of-612-hours-of-work/149730 for overviewOpenStreetMap
I spent two years mapping the areas surrounding one of the most beautiful drives in the world. Each morning, I looked forward to discovering and mapping an area adjacent to a one I did the day before.
Spent the morning with the Bicycle wiki page and a 300-post thread about access=dismount, and the same thing kept coming up: our cycling data is really good at describing the road, and a bit less good at describing the person on the bike.
We tag the surface, the separation, the access. But the thing that decides whether I actually ride a route is rain, fatigue, and whether I trust the stretch where the cycle lane ends and the traffic doesn’t slow down. None of that is in the data, and it’s the part that matters on a Tuesday morning when it’s pouring.
I keep coming back to the idea that the gap between “legal to ride” and “would actually ride” is where the interesting mapping is. Maybe that’s a project for a quieter evening.
We tag the surface, the separation, the access. But the thing that decides whether I actually ride a route is rain, fatigue, and whether I trust the stretch where the cycle lane ends and the traffic doesn’t slow down. None of that is in the data, and it’s the part that matters on a Tuesday morning when it’s pouring.
I keep coming back to the idea that the gap between “legal to ride” and “would actually ride” is where the interesting mapping is. Maybe that’s a project for a quieter evening.
DinoData is a web-based tag editor for OpenStreetMap nodes, ways and relations, designed for phones first. Open an object, see what it is and what each tag means in your language, edit the tags with inputs suited to them, and save as a changeset.
OpenStreetMap data comes in several text formats. OSM XML is exact but verbose, OSM JSON has no comments, and Level0L is easy to type but has no grammar beyond its parser.
Written 2025-10-30… Published 2026-11-03
About three years ago, during my day-to-day mapping activities, I came across a golf course that had some obvious errors. I didn’t think much of it at the time. Just a fairway that was drawn right across the top of a green. But the more golf courses I looked at, the more frequently I encountered this error.
I soon learned about a golf course simulator video game that was built on OpenStreetMap data and was attracting a large influx of simulator enthusiasts who weren’t seasoned OSM mappers. Along with that came various tools to aid in converting OSM data into these simulators, as well as tutorial videos that made some very bad assumptions about how golf courses should be mapped. These factors set the stage for some very bad mapping practices to spread around the world.
My understanding is that attempts were made to correct these bad habits at the source, but the tutorial author wasn’t interested in revising or removing his video. Combine that with YouTube’s bad decision to hide down votes on videos, and you’re left with de facto training material that is widely disliked but that new golf course mappers have no way of knowing is a poor source of truth.
So this is where I come on the scene and try to make the map better.
I’m a big fan of the broken windows theory. I’ve seen it play out in my own town. A minimally maintained but abandoned pizza parlor near me stood for years waiting for a new occupant to lease it. You’d see prospective tenants touring it every once in a while, but no takers. Eventually, someone tagged the building with graffiti. It wasn’t cleaned up in a timely manner, and things spiraled downward from there: more graffiti, broken windows, stolen copper, garbage left behind by homeless people, and eventually, the building was razed.
I saw similarities with golf courses. This broken “style” of drawing a fairway, intersecting the green, crossing over it, and then intersecting the green again on the way out to the rest of the fairway was becoming commonplace. I fixed one, then another, and another. I thought about contacting the original mappers and educating them, but these were often edits made years ago, and the mappers were probably no longer active.
This is when I came up with my “Broken Greens Theory.” Any green that was “broken” could lead to more greens being “broken” when someone stumbled upon it and thought, “Oh, so that’s how you map a fairway and a green together.”
Any corrections would need to be made soon after the error was introduced, while the edits were still fresh, the mapper was possibly still active, and the damage was limited in scope. At the same time, existing errors would have to be fixed as well, or we’d never reach a point where people weren’t learning bad practices from the existing maps.
I’ve learned a lot over the last three years working on this project, from editing techniques in JOSM (even though I was already well-versed in its use) to spatial database queries. One of the neatest things I learned was about the Dimensionally Extended 9-Intersection Model and how it could be used to query for specific ways that fairways intersected with greens.
I figured out how to construct PostGIS SQL queries to identify all of these intersections and was shocked to find well over 20,000 of them in the United States. I put together tools to group these intersections and feed them, course by course, into JOSM to be manually edited and saved back out.
At the start of the year, I set a goal of fixing this one specific type of golf course error by the end of the year. As I progressed through the year, the number of remaining fixes dropped, and it looked like I was on track. Then I started noticing some intersections on nearby holes, close to ones I was already working on, that hadn’t been caught. Uh oh.
My query only caught intersections where the fairway line crossed into the green and then back out. It wasn’t catching cases where the fairway shared a node with the green, followed the green for one or more nodes, and then crossed into and back out of the green. I was disheartened to revise my query and discover that several thousand more pairs with this slightly different intersection pattern had been overlooked.
I doubled down on my efforts and increased my daily goal. A few months later, I discovered yet another type of mapping error. Some fairways shared every single point as they went around the outside of the green, where a green’s fringe would normally exist.
So I repeated my routine: fixing the query, being disheartened by the discovery of thousands more errors, and redoubling my efforts to get the job done by the end of the year.
Along with all of these edits, when my wrists could no longer take the repetitive strain for the day, I would work on developing more tools to download daily map diffs for the entire globe.
Things are different in the diff world, no pun intended. You can’t simply load diffs for the United States into the database and query them. But I wrote some code to process the XML stream and handle the unusual way that changes are grouped together. I developed tools that would examine changes to golf courses in the target area of the United States, load only those changes into my database along with nearby data, test the new edits for errors, and generate a “report” of edits that warranted further investigation by hand.
Since the start of the year, I’ve identified about 300 cases of people making “bad” edits that were significant enough that I felt reaching out and working with the mapper to correct their behavior would benefit the community.
The first thing I discovered is that changeset comments are almost useless. I’m guessing that over 90% of them are completely ignored. It’s unclear whether they were even read in the first place. I’d love to see someone put together a study on the effectiveness of changeset comments, especially when directed at newer or “drive-by” mappers.
I started requesting blocks for some of these repeat offenders from the Data Working Group and had some success getting their attention through that method. However, some mappers were combative and felt that OSM needed to bend to their will. They were challenging conversations, to say the least.
Over time, I’ve learned that messaging a mapper directly has a much better ROI, but still nowhere near 100% effective. I believe they see the unread message count and are more likely to click on it than to check for changeset comments in an email inbox that they probably use only for spam.
As the year went on, I started seeing fewer and fewer of these fairway/green intersections. Possible explanations include there being fewer courses left to map from scratch, or perhaps it’s the weather, as the season changes and people spend less time mapping. It will be interesting to see how the numbers change over the next year.
With these two approaches to tackling the problem (fixing existing issues and finding new ones), I’ve finally finished fixing every single fairway/green intersection in the United States.
I estimate that I’ve fixed over 30,000 fairways over the past few years. I consider that a big success, and I hope that ongoing maintenance will be quick and manageable.
About three years ago, during my day-to-day mapping activities, I came across a golf course that had some obvious errors. I didn’t think much of it at the time. Just a fairway that was drawn right across the top of a green. But the more golf courses I looked at, the more frequently I encountered this error.
I soon learned about a golf course simulator video game that was built on OpenStreetMap data and was attracting a large influx of simulator enthusiasts who weren’t seasoned OSM mappers. Along with that came various tools to aid in converting OSM data into these simulators, as well as tutorial videos that made some very bad assumptions about how golf courses should be mapped. These factors set the stage for some very bad mapping practices to spread around the world.
My understanding is that attempts were made to correct these bad habits at the source, but the tutorial author wasn’t interested in revising or removing his video. Combine that with YouTube’s bad decision to hide down votes on videos, and you’re left with de facto training material that is widely disliked but that new golf course mappers have no way of knowing is a poor source of truth.
So this is where I come on the scene and try to make the map better.
I’m a big fan of the broken windows theory. I’ve seen it play out in my own town. A minimally maintained but abandoned pizza parlor near me stood for years waiting for a new occupant to lease it. You’d see prospective tenants touring it every once in a while, but no takers. Eventually, someone tagged the building with graffiti. It wasn’t cleaned up in a timely manner, and things spiraled downward from there: more graffiti, broken windows, stolen copper, garbage left behind by homeless people, and eventually, the building was razed.
I saw similarities with golf courses. This broken “style” of drawing a fairway, intersecting the green, crossing over it, and then intersecting the green again on the way out to the rest of the fairway was becoming commonplace. I fixed one, then another, and another. I thought about contacting the original mappers and educating them, but these were often edits made years ago, and the mappers were probably no longer active.
This is when I came up with my “Broken Greens Theory.” Any green that was “broken” could lead to more greens being “broken” when someone stumbled upon it and thought, “Oh, so that’s how you map a fairway and a green together.”
Any corrections would need to be made soon after the error was introduced, while the edits were still fresh, the mapper was possibly still active, and the damage was limited in scope. At the same time, existing errors would have to be fixed as well, or we’d never reach a point where people weren’t learning bad practices from the existing maps.
I’ve learned a lot over the last three years working on this project, from editing techniques in JOSM (even though I was already well-versed in its use) to spatial database queries. One of the neatest things I learned was about the Dimensionally Extended 9-Intersection Model and how it could be used to query for specific ways that fairways intersected with greens.
I figured out how to construct PostGIS SQL queries to identify all of these intersections and was shocked to find well over 20,000 of them in the United States. I put together tools to group these intersections and feed them, course by course, into JOSM to be manually edited and saved back out.
At the start of the year, I set a goal of fixing this one specific type of golf course error by the end of the year. As I progressed through the year, the number of remaining fixes dropped, and it looked like I was on track. Then I started noticing some intersections on nearby holes, close to ones I was already working on, that hadn’t been caught. Uh oh.
My query only caught intersections where the fairway line crossed into the green and then back out. It wasn’t catching cases where the fairway shared a node with the green, followed the green for one or more nodes, and then crossed into and back out of the green. I was disheartened to revise my query and discover that several thousand more pairs with this slightly different intersection pattern had been overlooked.
I doubled down on my efforts and increased my daily goal. A few months later, I discovered yet another type of mapping error. Some fairways shared every single point as they went around the outside of the green, where a green’s fringe would normally exist.
So I repeated my routine: fixing the query, being disheartened by the discovery of thousands more errors, and redoubling my efforts to get the job done by the end of the year.
Along with all of these edits, when my wrists could no longer take the repetitive strain for the day, I would work on developing more tools to download daily map diffs for the entire globe.
Things are different in the diff world, no pun intended. You can’t simply load diffs for the United States into the database and query them. But I wrote some code to process the XML stream and handle the unusual way that changes are grouped together. I developed tools that would examine changes to golf courses in the target area of the United States, load only those changes into my database along with nearby data, test the new edits for errors, and generate a “report” of edits that warranted further investigation by hand.
Since the start of the year, I’ve identified about 300 cases of people making “bad” edits that were significant enough that I felt reaching out and working with the mapper to correct their behavior would benefit the community.
The first thing I discovered is that changeset comments are almost useless. I’m guessing that over 90% of them are completely ignored. It’s unclear whether they were even read in the first place. I’d love to see someone put together a study on the effectiveness of changeset comments, especially when directed at newer or “drive-by” mappers.
I started requesting blocks for some of these repeat offenders from the Data Working Group and had some success getting their attention through that method. However, some mappers were combative and felt that OSM needed to bend to their will. They were challenging conversations, to say the least.
Over time, I’ve learned that messaging a mapper directly has a much better ROI, but still nowhere near 100% effective. I believe they see the unread message count and are more likely to click on it than to check for changeset comments in an email inbox that they probably use only for spam.
As the year went on, I started seeing fewer and fewer of these fairway/green intersections. Possible explanations include there being fewer courses left to map from scratch, or perhaps it’s the weather, as the season changes and people spend less time mapping. It will be interesting to see how the numbers change over the next year.
With these two approaches to tackling the problem (fixing existing issues and finding new ones), I’ve finally finished fixing every single fairway/green intersection in the United States.
I estimate that I’ve fixed over 30,000 fairways over the past few years. I consider that a big success, and I hope that ongoing maintenance will be quick and manageable.
Ive been building Wandering Around, a free browser game in which you can walk around any town or city on Earth. The whole world comes from OpenStreetMap, so Id like to share how it uses OSM data and ask this community for feedback.
While walking around, surveying, Ive often theorised it would be possible to work out where I lived based on the centre of all the edits Ive made.
X marks the spot to the crossroads of America. Route 30 travels east to west and Route 41 north to south spanning the whole country. Schererville, Indiana
X marks the spot to the crossroads of America. Route 30 travels east to west and Route 41 north to south spanning the whole country. Schererville, Indiana
X marks the spot to the crossroads of America. Route 30 travels east to west and Route 41 north to south spanning the whole country. Schererville, Indiana
A fantastic guide. But my god, could it make the changeseys a bit more bulky instead of creating a new one for every single edit?
I feel like I’m spamming now…
I feel like I’m spamming now…
I had a great time at SotM LATAM in Mexico City this past week. I got to hear so many talks on diverse subjects, I met friendly and interesting people, and there was a general acceptance that a random person from Canada would be there. Kudos to the organizing team and volunteers who put on a smooth, well run event.
I have never been to a State of the Map before despite thinking about going since way back in 2016 when I happened to already have a visa for Brazil. That certainly would’ve been harder with my level of Spanish understanding then. I will definitely be back.
Gracias a la comunidad y hasta la próxima. Espero encontrar oportunidades para colaborar.
I have never been to a State of the Map before despite thinking about going since way back in 2016 when I happened to already have a visa for Brazil. That certainly would’ve been harder with my level of Spanish understanding then. I will definitely be back.
Gracias a la comunidad y hasta la próxima. Espero encontrar oportunidades para colaborar.
Surprised myself and participated for rrrreal! (well, ok, missed the first day)
Registered in 2014, but I only started using OpenStreetMap actively last year.
After watching some Aboriginal movies of Australia, I switched from the Himalayas to Down Under, and then with chasing tracks in the outback of Australia—uncovering hidden reservoirs, basins, homesteads, cattle stations, farms, and surprisingly, some generous peaks. From there, I switched over to China, where the clever use of land through greenhouse horticulture stands out as one of the best examples of resourcefulness.
Flipping to Ethiopia brings a blend back to Nature: homes and huts constructed from natural elements offer a grounding perspective, even as the region slowly turns toward the so-called “blinded modernization.” Crossing over to South America, dense forest covers reveal hidden streams and alpine lakes tucked high in the mountain ranges, though the sheer scale of deforestation is disheartening to see.
Jumping to Japan and the Korean peninsula, the structured, disciplined approach to farming is among the finest in the world, matched by remarkable urban and human development.
Further north, the vast expanses of Alaska and Siberia ground you completely—reminding us of our fragile place within Nature and how quickly our so-called "development" will simply be reclaimed by time.
OSM has helped keep me calm, though it is still easy to get agitated. Now, I’ve even started mapping via my mobile browser as well, which has been a lifesaver while traveling on public transport. In a world full of towering billboards and aggressive roadside slogans, mapping on the move gives me the perfect excuse to ignore the visual pollution.
Of course, whenever I'm passing through true valleys and breathtaking landscapes, the phone goes straight into my backpack so I can take it all in.
After watching some Aboriginal movies of Australia, I switched from the Himalayas to Down Under, and then with chasing tracks in the outback of Australia—uncovering hidden reservoirs, basins, homesteads, cattle stations, farms, and surprisingly, some generous peaks. From there, I switched over to China, where the clever use of land through greenhouse horticulture stands out as one of the best examples of resourcefulness.
Flipping to Ethiopia brings a blend back to Nature: homes and huts constructed from natural elements offer a grounding perspective, even as the region slowly turns toward the so-called “blinded modernization.” Crossing over to South America, dense forest covers reveal hidden streams and alpine lakes tucked high in the mountain ranges, though the sheer scale of deforestation is disheartening to see.
Jumping to Japan and the Korean peninsula, the structured, disciplined approach to farming is among the finest in the world, matched by remarkable urban and human development.
Further north, the vast expanses of Alaska and Siberia ground you completely—reminding us of our fragile place within Nature and how quickly our so-called "development" will simply be reclaimed by time.
OSM has helped keep me calm, though it is still easy to get agitated. Now, I’ve even started mapping via my mobile browser as well, which has been a lifesaver while traveling on public transport. In a world full of towering billboards and aggressive roadside slogans, mapping on the move gives me the perfect excuse to ignore the visual pollution.
Of course, whenever I'm passing through true valleys and breathtaking landscapes, the phone goes straight into my backpack so I can take it all in.
OpenHVRA: a free screening level hazard, vulnerability and risk assessment tool built on OpenStreetMap
OpenHVRA: a free screening level hazard, vulnerability and risk assessment tool built on OpenStreetMap
Title: OpenHVRA: a free hazard, vulnerability and risk assessment tool built on OpenStreetMapOpenStreetMap
Motion extraction tutorial: https://youtu.be/NSS6yAMZF78?t=226
some casual oceania reef mapping
Motion extraction tutorial: https://youtu.be/NSS6yAMZF78?t=226OpenStreetMap
And just like that, we reached the final day of OSM LATAM 2026 México! 🚀
Day 2 of OSM LATAM 2026 México brought us another incredible day of learning, collaboration, and community!
1) In Vespucci, create a custom layer named Strava-cookies with the following URL: https://content-a.strava.com/identified/globalheat/all/hot/{z}/{x}/{y}.png?px=256.
Adding the Strava Heatmap layer in Vespucci (using personal cookies)
1) In Vespucci, create a custom layer named Strava-cookies with the following URL: https://content-a.strava.com/identified/globalheat/all/hot/{z}/{x}/{y}.png?px=256.OpenStreetMap
OSM LATAM 2026 México 🇲🇽 — Day 1: Connecting, Learning, and Mapping Together!
I had the wonderful opportunity to attend OSM LATAM 2026 México, and what an amazing first day it was!
It was a pleasure to meet so many incredible mappers, connect with fellow community members, and share knowledge, experiences, and ideas around OpenStreetMap. I also enjoyed participating in community talks and learning about the new technologies and tools being developed to further support OSM and its community.
One of my personal highlights of the day was discovering Maprizon, a state-of-the-art street-level imagery platform from Kaart, and fAIr, a collection of AI-assisted mapping tools from HOT. It was exciting to learn about these developments and explore how emerging technologies can help us improve our mapping workflows and contribute even more effectively to OpenStreetMap.
Events like this remind us that OpenStreetMap is much more than a map. It is a global project powered by people, collaboration, shared knowledge, and a common passion for open geographic data.
A heartfelt thank you to all the sponsors, allies, organizing committees, speakers, volunteers, local governments, venue staff, and all the amazing people attending and making this event possible.
It has been an inspiring first day, and we’re already looking forward to continuing tomorrow, learning more, exchanging ideas, and honing our mapping skills together!
OSM LATAM 2026 México! 🗺️❤️
#OpenStreetMap #OSMLATAM #OSMLATAM2026 #Maprizon #fAIr #MappingTogether
I made Walkably, a free walkability map for 31 European countries.
I lost my wife because of cancer on 22 September. Half of my soul and motivation also lost with her..
PillarPoint: an open source Android app for trig points and bench marks
OpenStreetMap is a collaborative mapping project that allows people around the world to explore maps and contribute geographic information. Community members work together to improve map data and make geographic information more accessible.
