Hi Arjun,

Really interesting approach. Using OSRM to route-match segment-based telematics data is an elegant solution to what is otherwise a frustrating data quality problem. The 19 unmapped road candidates from just two months of data shows how much potential there is in this kind of pipeline.

I am curious whether you see this scaling beyond Kerala. Commercial vehicle telematics is a global dataset; logistics operators, bus fleets, and freight companies exist everywhere, and many regions have the same gap between what is in OSM and what vehicles are actually driving. A replicable, documented workflow (your Python, OSRM and gpxpy stack sounds like a good basis) could be valuable for OSM communities in other countries, particularly where aerial imagery is sparse or outdated.

Have you thought about packaging this up as a reproducible tool or writing it up more formally? Publishing it on Codeberg or GitHub with a good README and some example data would make it easy for other OSM communities to pick up and adapt. It feels like the kind of thing that could get traction well beyond Kerala.

One thing worth considering as the pipeline scales is privacy. Even anonymised commercial vehicle traces can potentially reveal driver behaviour, working patterns, or commercially sensitive routes. It may be worth documenting the consent chain from the telematics provider through to OSM contribution, and noting any anonymisation steps taken, such as stripping timestamps before upload. This would also strengthen the case for wider adoption.

Great work, and good luck with the JOSM review of the unmapped candidates.

Chris

Interesting post, and the quarry example really illustrates the point well. The physical feature persists even when the function has ended, which is quite different from a closed restaurant.

Janjko’s apartment building example points at the same underlying principle: the lifecycle prefix system works well where the thing genuinely ceases to exist as that thing (a closed restaurant is no longer a restaurant), but becomes awkward where the form remains while only the use has changed. In those cases disused=yes as a modifier feels more honest than prefixing the primary tag.

One dimension I don’t think has been fully explored in the comments is reversibility. disused=yes seems to carry an implicit sense that something could return to use (the empty flat, the fountain awaiting repair), whereas disused:amenity=* implies something more permanent. Whether that distinction is intentional or just emergent from mapper habit is probably worth documenting somewhere.

Thanks,

Chris

I can see the village is now mapped. Have you thought about adding local knowledge, using StreetComplete? https://streetcomplete.app/

You will need access to a smartphone to do this.

Thanks,

Chris.

The OSM tag wiki should help. I find this from one of the linked reports:

https://taginfo.geofabrik.de/europe:united-kingdom/tags/plant%3Asource=solar#map

Chris

Nice work.

Thanks for the clarification Marcos, makes sense to keep the edits manual for accuracy.

I had a go at tidying up the script a little, in case it’s useful. The main changes:

• Swapped os.system() for subprocess.run() — a bit safer and more Pythonic
• Added an input() pause between objects so they open one at a time rather than all at once
• Used a context manager for the DB connection
• Added a HAVING count(*) < N filter to focus on the long tail and skip common valid values
• Added a KNOWN_GOOD set to skip values you already know are fine
• Added comments explaining the negative-ID-means-relation behaviour (I didn’t know that, good to learn!)

Revised script below. Sorry about the formatting, the diary interprets hash/ pound as bold font, they should be comments in the script. Happy to be ignored if you prefer your original — it clearly does the job! :)

Chris

Revised script

```python #! /usr/bin/env python3 “”” fix_osm_tags.py - Find and manually correct wrong highway tag values in OSM.

Requires a local osm2pgsql rendering database (e.g. ‘europe’). Workflow: 1. Queries planet_osm_line for highway values, rarest first (long tail first). 2. For each rare value, opens the OSM editor in your browser one object at a time. 3. You review, correct or leave a note, then press Enter to continue. “””

import subprocess import sys

import psycopg2

— Configuration —

DB_NAME = “europe” BROWSER = “librewolf” BROWSER_PROFILE = “default”

Highway values that are known-good and should be skipped.

# Expand this list to avoid being prompted for valid rare values. KNOWN_GOOD = { “residential”, “track”, “path”, “footway”, “cycleway”, “service”, “unclassified”, “tertiary”, “secondary”, “primary”, “trunk”, “motorway”, “living_street”, “pedestrian”, “steps”, “motorway_link”, “trunk_link”, “primary_link”, “secondary_link”, “tertiary_link”, }

Only show groups with fewer than this many occurrences.

# Keeps the focus on the long tail of rare/likely-wrong values. MAX_COUNT = 50

def open_in_editor(osm_id: int) -> None: “"”Open the OSM web editor for a given osm2pgsql osm_id.

In osm2pgsql rendering databases, negative IDs represent relations;
positive IDs represent ways.
"""
if osm_id < 0:
    # Negative ID => relation
    url = f"https://www.openstreetmap.org/edit?relation={-osm_id}"
else:
    # Positive ID => way
    url = f"https://www.openstreetmap.org/edit?way={osm_id}"

subprocess.run([BROWSER, "-P", BROWSER_PROFILE, url], check=False)

def main() -> None: with psycopg2.connect(dbname=DB_NAME) as db: cursor = db.cursor()

    # Fetch highway values ordered by frequency ascending (rarest first).
    # HAVING filters out common values that are unlikely to be errors,
    # keeping the focus on the suspicious long tail.
    cursor.execute(
        """
        SELECT
            count(*) AS count,
            highway
        FROM planet_osm_line
        WHERE
            highway IS NOT NULL
        GROUP BY highway
        HAVING count(*) < %s
        ORDER BY count ASC
        """,
        (MAX_COUNT,),
    )
    groups = cursor.fetchall()

    for count, highway in groups:
        # Skip values we already know are valid.
        if highway in KNOWN_GOOD:
            continue

        print(f"\n{'='*50}")
        print(f"Value: '{highway}'  ({count} occurrence{'s' if count != 1 else ''})")
        print("Press Enter to open each object, or type 's' to skip this group.")

        choice = input("> ").strip().lower()
        if choice == "s":
            continue

        # Fetch all OSM IDs with this highway value.
        cursor.execute(
            """
            SELECT osm_id
            FROM planet_osm_line
            WHERE highway = %s
            """,
            (highway,),
        )
        osm_ids = [row[0] for row in cursor.fetchall()]

        for i, osm_id in enumerate(osm_ids, start=1):
            print(f"  Opening {i}/{len(osm_ids)} (osm_id={osm_id}) ...")
            open_in_editor(osm_id)

            # Note: if the object no longer exists in OSM, the editor
            # will open a view of the whole planet — just close that tab.
            if i < len(osm_ids):
                next_action = input("  Press Enter for next, or 's' to skip rest of group: ").strip().lower()
                if next_action == "s":
                    break

    print("\nAll done!")

if name == “main”: sys.exit(main()) ```

Nice script, Marcos! Would you be able to share an example of a ‘before’ and ‘after’ tag?

Thanks,

Chris

Excellent job, well done.

Chris

Well done, have you tried StreetComplete? https://streetcomplete.app/

Chris

You’re doing a really good job, Marcie. Re the addresses, if you’ve got an Android smartphone, I think having a look at StreetComplete https://streetcomplete.app/?lang=en-gb would help.

Take care,

Chris

Hi, Marcie.

Well done for taking your first foray into editing the best map in the world! You’re bound to make some mistakes as you go along, we all do.

Something to watch out for, is importing information from sources that don’t have a compatible licence. The government map you refer to, may have some sort of proprietary licence; it’s best, if possible, to survey in person, and update OSM with what you can see. The other downside of relying on other maps, is you may introduce errors to OSM, from poorly verified local maps. Just something to watch out for.

Not sure whether you’ve heard of it, but an app called StreetComplete is good for adding information.

Good luck,

Chris (UK)

Cool, have fun. Don’t forget the quick and easy updates that you can do, using the StreetComplete smartphone app.

https://streetcomplete.app/

Chris

Perhaps add this tag, to show the previously used name?

osm.wiki/Key:old_name

Chris

This is new to me, but thanks for highlighting it.

Chris

Hi, Ray.

Good work! To help your efforts, are you aware of the RapidID editor? It analyses the mapping imagery and suggests houses, etc. https://rapideditor.org/

This of course, should not replace common sense, and if using this method, it should be done in small batches. Mass/ automated editing is strongly discouraged, and just because Rapid identifies something, it may not be correct; you should always be sure, before committing the change.

Happy mapping.

Chris

Hi, this sounds interesting. Are you aware of the useful tools, here: ohsome - dashboard https://share.google/PiwlQi1k512agz7ec

Thanks,

Chris

Hi, rphyrin.

That’s a really good idea, especially the reuse of your code. I’m not a developer, but am trying to get MOROW off the ground. I have two versions; one based on old code, which I couldn’t get to build, and one created in Flutter, which I’ve just started. Any thoughts you have, would be greatly received.

https://github.com/chrisdebian

Chris

Jason, I meant to say, have you tried the StreetComplete app? https://streetcomplete.app

Cheers,

Chris

OSM devs, I wonder how we get this on the backlog, to factor into future planning? Jason makes valid points. I tried the accessibility checker, but it came up with a paywall, for me.

Cheers,

Chris chris_debian 2E0FRU

Good point, Alan.

I guess there would have to be a minimal acceptable imagery resolution, for a Quest to be available.

Thanks,

Chris