The most common error we end up doing while splitting well-mapped roads in a highly mapped area is by breaking a route relation. This usually involves breaking the continuity of a bus or highway route due to missing members. The community has reported broke route relations while the data team was trying to improve navigation features (Turn lanes & turn restrictions) in US cities. On thorough research found some points to share with you all and hope this helps in solving route relations breakages in future.
The first thing to note, there is nothing wrong with any of the split way or knife-tool that we use to split the road. In general, route relations are very long and spreads across the city. When two or more persons work at the same time, on editing roads part of the same route relation, JOSM will throw relation edit conflict. This conflict is very specific on which version of the edit to keep and which one to remove. In general, people tend to resolve the conflict by pushing their edits and skipping the other’s edits. This causes the route relation breakage. Let me explain clearly with examples.
For this use case, I took a bus route (Relation: 333P) in Bangalore, India and tried to download the data into JOSM at two corners of the route into two different layers (layer-1 & layer-2). Now the route relation 333P has 89 members at version-61.
Route Relation: 333P in Bangalore with the location of a road to split in layer-1 and layer-2
The OSM server rejects the upload since it has a newer version of the relation
Current version (my edits) and OSM version in Resolve conflict tab
After pushing my current edits without considering the other version in OSM
Resolving relation conflicts can be challenging since it can be difficult to get the context of the edit in a list of object id’s. If JOSM can be intelligent enough to preserve the continuity of a relation over conflicting edits, then it can identify the missing member and add it to the relation without causing the relation to break. Want your ideas on how we can resolve this issue following a much simpler approach.
This is one case I came across and there might be many such cases where even inexperienced mappers may be unknowingly breaking relations if they are not familiar with proper conflict resolution. Please feel free to post your thoughts on this and share any easier workflow you may have to resolve such conflicts.
P.S. Always before uploading modified data into OSM check for their updates on OSM just by pressing ⌘+U for MAC, cntrl + U for Windows and Linux users (Update data) 🖐️
cntrl + U
OpenStreetMap is a great place where we can share the on-ground information with the community. Most of the time the data we add is good, but sometimes we might end up adding bad data. We might even come across unspecified bad imports which are supposed to be reverted. To overcome this we have a reverter plugin in JOSM, which helps in reverting data on changeset basis. The plugin works fabulously in reverting small changesets but fails to revert huge changesets with the below popup:
There is no proper documentation for reverting huge changesets. On through research found that it’s because of the less socket.timeout given by default in JOSM. If we adjust the socket.timeout then the reverter plugin started to work well with big datasets. The below values worked for me.
You can adjust your socket.timeout by going to –> JOSM > preferences > advanced preferences
JOSM > preferences > advanced preferences
For more tools to revert, see the OSM Wiki -> http://wiki.openstreetmap.org/wiki/Change_rollback
Hope my findings help you with better mapping. Let me know if there is any other simpler way to revert as there is no proper documentation for reverting huge changesets.
San Francisco, Washington DC and Los Angeles just got 51,385 turn lanes added to the map over the last 3 months. This was part of the push by the Mapbox data team to enrich OSM for better navigation and guidance.
Turn lanes added in LA
All the mapping tasks were coordinated and documented on GitHub. We’re simultaneously working to create a navigation mapping guide to consolidate a lot of documentation on the wiki with added context for the areas we work on. This should make it easy for new mappers improve OSM in their areas and also help potential users of the map data understand the tagging model. The comments and suggestions from the community helped us a lot in improving the guide and to speed up the mapping process.
Split up of turn lanes added
Please share your feedback on our changesets or at the Mapbox/mapping repository.
In my previous hometown mapping of Rajahmundry post, I pointed out some of the data errors present. I contacted most of the OSM contributors and found that most of the data was added as part of a local muncipality project. It is nice to see that my local municipality is adopting open data in order to reach citizens and it will be the best opportunity to provide awareness of open data among the local citizens.
The data they added were from local surveys. I personally verified some points and I can attest that the locations are accurate, however, I found several cases of wrong tagging. For example, they mostly used the Name instead of name. According to OSM convention, keys should be the lower case.
Wrongly tagged point features
To fix this, I extracted all the point features within the city using overpassturbo and exported that data into JOSM. There were a total of 480 point features tagged as Name instead of name. I selected all the point features in JOSM and retagged them with name tag. The names are now properly rendered in OSM.
Names are now rendered, but more work is needed
Suggestions and comments from the OSM community members are always welcome. Happy mapping.
Myanmar is witnessing an unprecedented growth in international tourist arrivals following major democratic reforms since 2011. On OSM, around 70,000 kms of roads have been mapped so far, or roughly 42% of the total reported roadways.
Most of the major highways (motorways, trunk, primary, secondary and tertiary roads) are well mapped in the major cities of Yangon, Mandalay, and Naypyidaw, but there are a lot of missing residential and unclassified roads visible in the imagery. Over a period of three weeks, the Mapbox data team was able to add a total of 7,690 missing streets, out of which 2559 and 5131 streets were added in Mandalay and Yangon respectively.
Missing streets in Mingaladon locality in Northern Yangon
A full map of all objects added or modified by the Mapbox data team.
Dhammayangyi temple in Myanmar. PD user:Hintha
In India, there are ~7935 cities/towns officially recognized by the Government of India according to the 2011 census. But in OSM, there are only 3352 (as of 2015-11-23) cities/towns mapped as place node.
As the first step of mapping, it is important to mark all the major cities and towns in the country. The Mapbox data team did some background analysis of the data in OSM and compared it to other publicly datasets. This guide will walk through the step of using a public domain map layer from geonames.nga.mil for improving the location of India’s towns/cities in OSM using JOSM.
changeset comment: "Add place town nodes"
source comment: "Bing, geonames.nga.mil WMS"
Rajahmundry is one of the most populated cities in Andhra Pradesh, India. It is located on the banks of Godavari river and it is most known for its high cultures and traditions. Rajahmundry is also known as the ‘Cultural Capital’ of Andhra Pradesh.
I started my OSM mapping journey recently and the first thing I wanted to map is my hometown (Rajahmundry). In the beginning, I was quite surprised to see that most of the roads and buildings of the city are already mapped by OSM mappers for which I’m always thankful. This made my work much simpler and thought of adding names to the streets which I’m familiar with, but soon I started to encounter errors in the data. The most common error is the misalignment of data, which I mentioned in the following context.
1. The difference in Satellite data
The city is having overall coverage of Bing imagery but the Satellite data is divided into three major blocks. The center block is from 2013 coverage, the upper block is from 2012 coverage and the lower right block is from 2011 coverage.
There is a significant shift at the intersection of these blocks
This is the major reason that caused data alignment problems in this city.
2. Misaligned roads
Due to the shift in the merging of satellite data the roads in this blocks are completely misaligned. Most of the roads are aligned to the older satellite imagery and the others are aligned with the newer satellite imagery. This brought a lot of mess in the data.
3. Building digitization
Most of the buildings at the heart of the city are digitized by different contributors but are digitized in different time periods which caused data alignment problem. Also, the satellite image is not that clear to identify the building footprints, which made it hard for the contributors to identify the exact building dimensions I guess.
4. Misplaced Hospitals and medical shop’s
The only thing one can identify by looking at the cities data is Medical symbols marked all around the city. I’m sure that there are many hospitals and medical stores in the city but not as many as marked on the map. I personally went to the marked locations of some medical stores but failed to find most of them. It seems like all the medical related shops and hospitals are imported by a single user whom I need to contact and know the details of the imported data.
Cleaning up the data without a proper understanding of the background is pretty hard. In the beginning, I started to align the roads to the Bing imagery, but later I found the shift in the imagery itself which spoiled my previous work. I tried to delete all the duplicate roads and nodes. I did not touch the building alignment work as the imagery is not clear enough to mark the building footprint (hoping to get clear imagery soon). I also made some changes to the borders of the Rajahmundry Airport. I aligned the roads with the available imagery but I don’t think it’s the best process to follow.
All together my edits look as below, but there are a lot more to go