I had the opportunity to solely lead Nepal’s first Inclusive Mapping Week 2025 at Kathmandu University. What started as an idea became a week-long initiative bringing together over 400 participants to learn, map, and collaborate.

Throughout the program, we focused on both technical skills and real-world applications:

Humanitarian mapping using OpenStreetMap Remote sensing with Google Earth Engine Crisis mapping and spatial data use Field mapping with tools like Mapillary and OSM Tracker

One of the most meaningful aspects of this initiative was contributing mapping data to support earthquake-affected regions. It reinforced how open geospatial data can play a role in disaster preparedness and response.

As a young woman in the geospatial field, leading this initiative was both a challenge and a responsibility. Ensuring inclusivity and encouraging participation especially from women was at the core of this program.

Being recognized by The Annapurna Express for this work was a proud moment, but what matters most to me is the impact created through collective effort and open collaboration.

This experience taught me that:

Mapping is not just technical work—it creates real-world impact Leadership is about enabling others to contribute Inclusive communities build stronger, more meaningful maps

This is just the beginning of my journey with OpenStreetMap. Looking forward to contributing more, learning more, and building maps that represent people, communities, and realities. youthmappers blog; https://www.youthmappers.org/post/she-leads-we-map-nepal-s-first-inclusive-mapping-week-2025 The Annapurna Express: https://lnkd.in/g6ZSgBBq

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Introduction

Sometimes, we set out to solve one problem and arrive at a bunch of even greater discoveries along the way. This story starts with my curiosity about whether you can get a “GPS” track log underground - like in a tunnel or underground car park. GPS is our go-to tool for mapping most things that we can’t see on aerial imagery, but what can we do in places where GPS signals cannot be received? In the course of my investigation, I uncovered a few even more interesting insights:

• Even if you can code, it’s impressive what an off-the-cuff LLM prompt can build for you
• The openstreetmap.org site UI would work very differently had it been built in the smartphone era
• Capturing rich mapping data from stock vehicles with no extra hardware is feasible
• With relatively little effort, we can improve the effectiveness of GPS track log capture for OSM mapping

Oh, and I did manage to get that underground track log, but more on that anon…

Motivation: the desire to improve tunnel mapping

Mapping underground features in OSM can be challenging. Sometimes we are lucky - a tunnel or covered roadway may be a straight line between two known points on the surface. Perhaps the tunnel was built using cut-and-cover and we were able to establish the geometry during construction. But sometimes, we just have an underground linear feature with bends in it. We know where each end dives underground, but GPS signals cannot be received underground, so our traditional mapping approaches won’t help us.

Road tunnels, of course, are designed for vehicles, and many modern vehicles have moving map displays as part of a navigation system. When in a tunnel, many even show a plausible vehicle position that updates. Without GPS. How do they do that? They could simply infer movement along the mapped path of the road based on distance travelled. But they may also use more sophisticated dead-reckoning inferring direction from sensors. I have such a car. I wanted to find out.

… See full entry

In the second week (14th–19th February), we faced OSMMalawi. With no strategy to balance academics and mapping, I grew lazy. To overcome this, I wrote a sticky-note reminder on my laptop to push myself to map at least five tasks daily during breaks. By the end of the week, my contributions increased, and on 20th February, we celebrated another win, rising to 3rd place overall.

The third week (21st–26th February), the mapping match was against KabUyouth Mappers from Uganda. Bing imagery was unclear, but I adapted by using Google Earth references & comparing different imageries. My changesets piled up, promoting me from beginner to intermediate mapper. . We maintained the 3rd position but our captain organized a google meeting with Kingsley (one of the tournament organizers), who taught us valuable skills in both iD editor and JOSM.

By the fourth and fifth weeks (28th February–12th March), mapping had become part of my routine—even appearing in my dreams! Funny!!, am I right?

Despite some abrupt technical issues with OpenStreetMap login, we pushed through, won the game against YouthMappers Mukuba, and advanced to the next stage. By the end, we’re proudly ranked 4th among the top 10 contributing teams out of approx. 80 countries.

Thank you for reading my diary—I hope my journey inspires someone out there. Let’s map the world together! #SpatialMappers #AfricaMapCup2026. Cheers to all participants in this tournament, and please wish my team & I good for it’s still on going.

Every day at around 4 pm (unless there’s IRL business that I have to attend to), I log in to https://osmbc.openstreetmap.de/ to edit this week’s edition of WeeklyOSM.

My task is to review all the links submitted by both WeeklyOSM editors and guest users. I study each link, then write a short sentence describing it.

Some link submitters already accompany their links with proper sentences when submitting, so I mostly skip those. I only focus on links that don’t have English text yet.

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This afternoon, while doing my daily WeeklyOSM editing, I stumbled upon this MapComplete post announcing that it is now possible to add pictures to reviews on MapComplete. This feature is powered by Mangrove Reviews.

Then, I suddenly remembered a certain discussion thread on c.osm.org regarding the possibility of building “a crowd-sourced review service for OpenStreetMap.”

… See full entry

I remember when my captain and I searched for willing mappers in our community to register for the tournament, which required at least 20 participants per team. One colleague discouraged me, saying it was highly impossible for us to be among the winners. However, that didn’t stop me from learning JOSM and joining the tournament.

In the first match week (7th–12th February), my team faced Carto Afrique of Kenya. The transition from iD editor to JOSM was amazing—tasks that once took over an hour now took only 30–40 minutes, giving me time to complete more. JOSM’s validation tool saved us from penalties by detecting errors before uploading.

On 13th February, the results were announced: my team won against Carto Afrique! That victory gave us our first point, lifted our spirits, and placed us 5th among the top 10 contributing teams. Yet, as my semester began, I feared balancing mapping with academics, sports, and assignments which would be tough, making the experience even more intense. ……..thank you to those that are reading my dairy. comment your review and lets share our experiences.

The problem

I’ve been creating and serving web-based maps such as this one for some time. That’s based on raster tiles, and an osm2pgsql database is used to store the data that the tiles are created from, on demand as a request to view a tile is made.

For various reasons I wanted to also create a similar map using vector tiles. With vector tiles what is sent to the client (such as a web browser) is not lots of small pictures that the client stitches together, but instead larger chunks of data, still geographically separated. The client then creates the map itself based on the style that it has been told to show the data in, combined with the data itself.

I’d noticed that the vector maps that I was displaying were sometimes slow to load, especially at some lower zoom levels such as vector zoom 8. Note that vector zoom levels are one less than raster zoom levels, so vector 8 is raster 9.

… See full entry

The Beginning – Discovering JOSM..

I never imagined my mapping journey would reach this point in time. I would like to share with you my experience, which carried both doubts and excitement for my team and me—the thrill of learning Java OpenStreetMap (JOSM) and climbing the staircases that led to building victories in the Africa Map Cup 2026 Tournament. My name is Alvin Andrew Barugahara, also known as AlvinB (OSM name), a student from a mapping community in Uganda called Spatial Mappers at Ndejje University. I had always heard of JOSM and its simplicity in mapping OSM tasks. Back then, I was just a beginner mapper using iD editor, which was the default platform. It wasn’t bad, but it required constant internet access and had a small working window with few shortcuts, making mapping slow. My captain, Aikiriza Justus (OSM name), had a vision of teaching us how to use JOSM and become “advanced mappers.” He guided and pushed us beyond our limits through various Google meetings, preparing us for the Africa Map Cup 2026, which began on 7th February 2026. “Stay tuned for the next part of my Africa Map Cup journey…”

Solar Farms

Looking into getting a gist for where there are solar farm setups both on land and in water (lakes , ponds ) etc

Below I will outline improvements for data interoperability regarding Wilderness Study Areas in the United States: https://en.wikipedia.org/wiki/Wilderness_study_area

OpenStreetMap tagging:

• Main article: osm.wiki/United_States/Public_lands#Guidelines_for_tagging_conservation_areas)

Wikidata identifiers:

• Instance of: Wilderness study area
• Authority: BLM/USFS/etc.
• Inception
• Coordinates
• Described by source
• Area
• Official website
• Recreation.gov Gateway
• OSM Relation

Wikimedia Commons

• Wikidata Infobox template: {{Wikidata Infobox

  qid=}}

• Images
• Locator Maps
• PDFs of wilderness plan/study documents

Wikidata Queries: *https://w.wiki/MHst - Recreation.gov Gateway IDs with/without OSM identifiers *https://w.wiki/MJvN - Recreation.gov POIs *https://w.wiki/MLBD - Wilderness Connect IDs accompanied by WDPA, Recreation.gov Gateway; OSM identifiers

A few days ago, I asked the community about converting general GIS polygons into OSM multipolygon relations. I’ve searched online but haven’t found a workflow that fits my needs. Specifically, I am looking for a way to handle three different levels of administrative boundaries where adjacent areas share a single boundary line connected via a relation.

My question on the OSM forum is still awaiting a solution: Link

However, someone from my local community mentioned that what I’m looking for is “topology.” While that is a broad GIS term, they clarified that TopoJSON is a specific format designed for this. There are many converters available to turn GeoJSON into TopoJSON.

Interestingly, I found that someone opened a ticket for a TopoJSON converter in JOSM back in 2020, but it hasn’t received a response yet: Link

I’m planning to update and expand the administrative boundaries for Bali in OSM. I’ve already prepared the multipolygons for admin_level 5, 6, and 7 using single shared ways for efficiency. By leveraging Google Sheets, I’ve also compiled a comprehensive list of Wikidata, Wikipedia links, and multilingual names to better serve Bali’s international profile.

However, the conflation process is proving to be a challenge. The existing data is quite a “nightmare” to clean up; many roads and waterways are currently shared with administrative relations, and landuse or natural features are glued to the boundaries. Time to start untangling!

Mapping administrative boundaries in Indonesia can tricky especially when dealing with overlapping names. Here is my simplified workflow for preparing this data:

1. Data Sourcing

First, download the official spatial data from Peta Rupa Bumi by Badan Informasi Geospasial or searching it in Satu Data Indonesia. This serves as the primary geometry source.

2. Extracting Place Nodes

Since the source data is in polygon format, I use QGIS to extract the centroids (points). These points are essential for creating the place=* tags that represent the center of each administrative area.

3. The Importance of Kemendagri Codes

The polygons include Kemendagri reference codes. These are vital for:

• Conflation: Ensuring data matches across different sets.

• Identification: Many villages (admin_level 7 or 8) share the same name. The code helps distinguish them within a Regency or Province.

4. Enriching Metadata

Using spreadsheet tools and conflation techniques, I cross-reference the data to add:

• Postal codes

• Wikidata and Wikipedia tags.

• Multilingual names (name:en, etc.).

5. Geometry Processing

To follow OSM best practices, I convert the polygons into independent ways (polylines).

• This allows adjacent areas to share a single boundary line via a multipolygon relation.

• Once converted, I export the result as a .geojson file.

6. Final Tagging

Finally, I use the previously extracted place nodes to quickly copy and paste the relevant tags into the new multipolygon relations in my OSM editor.

🗺️ Entry 1 — Setting up JOSM & Plugins

Mapping Banjë, Albania

I started mapping the Banjë region in Albania by setting up my editing environment in JOSM.

⚙️ Setup

I configured JOSM with a set of plugins to support structured mapping and validation:

• utilsplugin2 – general productivity tools
• reltoolbox – relation and multipolygon editing
• waydownloader – working with connected geometries
• merge-overlap – cleaning overlapping features
• Relation Validation Plugin – checking data consistency
• FastDraw – faster geometry digitizing

I also explored additional plugins like contour-related tools for terrain-based mapping.

🗺️ Mapping Context

The focus area is Banjë (central Albania) — a landscape with: - Complex terrain (valleys, rivers, slopes)
- Mixed land use (forests, agriculture, settlements)
- Incomplete or inconsistent OSM coverage

🌱 Initial Observations

• Landuse classification is often fragmented or overlapping
• Boundaries between forest, farmland, and settlements are not always clear
• Many features require clean multipolygon structures
• Validation tools already highlight conflicts in relations

🎯 Next Steps

• Clean and structure landuse polygons (forest, farmland, residential)
• Resolve relation conflicts and validation errors
• Improve consistency of tagging using presets
• Start refining settlement structures and road connectivity

So, I’ve been using Altilunium LocationPad for several of my personal projects until now. But recently, I encountered several problems.

I dabble with multiple projects at once, but this app saves everything in a single database. I want this app to be able to create several separate “canvases”, so I can manage several of my projects at once, without mixing them with other projects.

… See full entry

I was recently reading Demographic deposit, dividend and debt by Sonalde Desai. Following the Standard Operating Procedure, I looked up this Sonalde person and turns out she is a researcher at a research institute in Delhi called National Council of Applied Economic Research. The work of this organization felt interesting to me, so I had to pause my studies and see its location on OpenStreetMap.

Nothing. Looks like the place hasn’t been mapped yet. I searched for 11, Indraprastha Estate since that was mentioned on their contact page and this time I did find an object. A person called “n’garh” had added the address back in July 2014 and I am so glad that I was quickly able to push a changeset (#180878526). Another win for address mapping!

This post was first released on my website with 💜 under CC BY-NC-SA 4.0.

Hey everyone,

the new version of UrbanEye3D, namely 2.0.0, is out!

This is a major update for the plugin. It’s been a lot of work and took about 2.5 months, but I’m confident it makes the 3D view significantly more useful and enjoyable to work with.

For those who haven’t heard of UrbanEye3D before: it’s a JOSM plugin that renders a 3D world from OpenStreetMap data directly inside JOSM. This lets you preview objects in 3D before uploading your changes to the OSM database.

What’s New

1. 2D Ground Plane

Buildings no longer float. The ground surface is displayed with “flat” objects - roads, lawns, rivers, and lakes. This flat layer is rendered based on downloaded OSM data using a custom MapCSS style. Alternatively, a satellite background can be enabled, as before.

2. Trees

… See full entry

I just published a hacky but effective tool to fix wrong tag values. This lead to some 50 edits only this morning.

More info: https://www.grulic.org.ar/~mdione/glob/posts/correcting-openstreetmap-wrong-tag-values/

Strado – Neighborhood Livability Scores from OpenStreetMap

I’ve been working on Strado, a free tool that scores every neighborhood across 50 European cities using data from OpenStreetMap. I wanted to share the project with the OSM community since it’s built entirely on your work.

What it does

Strado analyzes 22 categories of POIs from OSM – restaurants, healthcare, transit stops, parks, schools, nightlife, grocery stores, and more – and computes livability scores at the street level using an H3 hexagonal grid (resolution 9, ~174m edges).

The idea is simple: if you’re moving to a new city, you should be able to compare neighborhoods by what’s actually within walking distance. Not opinions, not sponsored listings – just data.

How it works

1. I imported the full Europe PBF into PostgreSQL/PostGIS using osm2pgsql with a custom flex style
2. POIs are extracted across 22 categories based on OSM tags (amenity, shop, leisure, healthcare, etc.)
3. Each H3 hex cell gets a count of nearby POIs with k=1 neighbor expansion
4. Livability scores are computed from essential categories (grocery, healthcare, transit, parks, education, safety)
5. Activity scores come from lifestyle categories (dining, nightlife, cafes, culture, shopping)
6. Everything is served as PMTiles vector tiles from Cloudflare R2 – no backend server needed

The scoring engine runs client-side in the browser using MapLibre GL JS. The entire infrastructure costs $0/month.

The data

• ~78 million POIs from OpenStreetMap
• 50 cities from London to Athens
• 22 scoring categories
• 20.5 million hex cells scored

Try it

• Interactive map – click any hexagon to see the score breakdown
• City pages – browse all 50 cities with neighborhood rankings
• Rome example – top neighborhoods, category breakdown, FAQ

All data is attributed to OpenStreetMap contributors under ODbL. Every page includes the attribution.

What I learned about OSM data quality

… See full entry