‘De Wasvrouwen’ is een beeld van Carla Rump in Groet, Noord-Holland.
Link naar de locatie met tags op OSM.
Het beeld is een creatie van Carla Rump.

多言語表記のタグ付けを考える(パート2: 編集合戦編)

2026-02-01に’さくらインターネット Blooming Camp’で行われた「マッパーズサミット2026」での発表内容の「編集合戦編」です

パート１: OSMの基礎知識編

この記事は「基礎知識編」の続編です

必ず「基礎知識編」を見てから「編集合戦編」へ進んでください

• パート１: OSMの基礎知識編

パート2: 編集合戦編

ここからは、OSM編集で実際に起きた「編集合戦」について説明します。

パート１を見ていない方は、パート１: OSMの基礎知識編 を先に見ください

事件は「渋谷スクランブル交差点」で起きました
- ウェイ: 渋谷駅前交差点 (1335178864) “バージョン #6” 2025-07-30

この交差点は英語圏では “Shibuya scramble crossing”として世界的に認知されており、インバウンド観光客の目的地ともなっています
- 渋谷スクランブル交差点

訪日客が”Shibuya scramble crossing”を目的地にした場合、数多ある渋谷周辺の’交差点’の中からどうやって”Shibuya scramble crossing” だと確信することができるでしょうか？
交差点にある「案内標識」の「国際表記名=Sybuya Sta.」がOSMに入力されていれば,日本語を解さない人でも「Sybuya Sta.」と表記された場所が「Shibuya scramble crossing」だと確信することができます

• ここでは、OSMwikiの「一般的な名称以外は、派生名に入れてください」に従い、国際表記部分を int_name=Sybuya Sta. と入力します
  • 「一般的な名称以外は、派生名に入れてください」@osmwiki:名称キー

… See full entry

OK. Last year I wrote a short guide on mapping Indian addresses but I lost it in my tiny pursuit to delete myself. Today I suddenly came across the fact that the guide was actually used by mappers and, hence, as a result I am now writing this post to become a replacement for that old guide. Since this is a new one, I don’t want to just rehash the old stuff and instead this time I am going to take a simple problem and show how I would solve it from scratch.

A1, Tower 2, Sector 11, RK Puram, South West District, Delhi, India

A problem very similar to this one came up in OSM India’s XMPP channel today. So, how does one go about mapping this address?

As it’s usually the case we can ignore the district, state, and country part as they are all very well mapped in India. This leaves us with everything upto RK Puram.

If you are thinking that something as big as RK Puram should surely be already on the map then you are wrong; In my “career” I have actually seen larger areas without any nodes for them. So we will in fact check if it’s already on the map and, guess what, it actually is already mapped as a suburb, so that’s one less step for us! I should mention that in OSM there are three “neighbourhood” levels below the district: quarter, suburb, and neighbourhood in decreasing order of size. In most cases suburb and neighbourhood should be enough for you, but it is important to be aware of quarter for special situations.

Now let’s check for Sector 11. As of writing this, Sector 11 isn’t on the map. So I will put a neighbourhood node at the approximate centre of Sector 11. (Remember that neighbourhood is smaller than suburb.) We are making good progress.

… See full entry

本系列编辑主要修复了部分住宅楼的过大幅度偏移，以及一些误标记的绿化。问题区域主要在星塘街以东，为方便起见，以东西向道路作阶段性的分割。

记录

2026/2/24已完成修复兆佳巷以北

2026/2/25已完成修复中新大道东以北

2026/2/26已完成修复港田路以北

A few months ago, I worked on a new project: the OSM Tagging Schema MCP — a Model Context Protocol (MCP) server built for AI assistants and LLM applications that interact with OpenStreetMap tagging data.

It serves as a bridge between AI systems and the official OpenStreetMap tagging schema, allowing agents to validate tags, query values, search presets, and suggest improvements using the structured knowledge from the @openstreetmap/id-tagging-schema library.

The current 3.x release is technically stable — all tools and endpoints work reliably without errors — but it should still be considered experimental. Active development on version 3 has ended; for now, I only maintain it through dependency updates.

The next major step will be version 4, a complete rewrite developed with AI-assisted coding, focusing on a cleaner architecture, long-term maintainability, and deeper MCP integration.

You can try the service live here: mcp.gander.tools/osm-tagging.

I invite you to experiment, test, and share feedback — your ideas and suggestions are always appreciated: gander-tools/osm-tagging-schema-mcp discussions

Before

After

… See full entry

finding myself to osm was something I couldn’t understand at the time. Now with an open eye, in my field of water engineering introduced to geographical information system. I love it here my journey begins now mapping take me overseas. 😊

I successfully put Novato Baylands Point Blue Conservation Science as a pin on the map. However, I have not had success with editing the directions that maps provides to get you to the site. The directions still route you past the facility, rather than stopping right at the facility. They should tell you to go down Aberdeen Rd, and then the location is on your right. Thanks for any assistance with editing the route.

I’m new to editing OpenStreetMap, so this is my first change! I noticed that most neighbourhood areas in Lethbridge, my local city, don’t have a name shown in OSM. However, they’re all neatly shown on an official 2024 map from the government of Lethbridge, so I used it as a source. I did notice that some areas are already named in other ways, but I couldn’t find the item that holds the name. This induced visual clutter by doubling some names (those of the industrial parks, Copperwood, and seemingly Paradise Canyon), but I still added the names to the neighbourhood areas for consistency anyways. If anyone around knows how to get rid of this without removing the naming consistency, it would be great if this slight issue could be resolved. I haven’t actually tested the map yet, since I just uploaded the edit, but if what I’m describing is actually a problem, please help? Anyway, I intend on updating and adding a lot of things to Lethbridge (like adding addresses and new buildings) in the near-ish future, so it’d be fun to get to know the local OSM community.

Code for Harimaの定例会で自分が表明したことで議事録に載せてますが、こちらに転記しておきます。

数ヶ月ほど前にあったCode for Harimaの定例会で、OSMを利用した万博マップに対して自分が「大阪万博には、赤字補てんのための公的資金投入やカジノありきの計画、パビリオン建設工事費未払問題などさまざまな政治的問題があり、それに加担するような活動は良くない」という発言したところ、参加していたOpenStreetMap Foundation Japanの某氏は「万博は政治的じゃない」と発言したことに驚いた出来事がありました。

OSMFJの人がそういう発言をするのであれば、自分は逆に 政治的なOpenStreetMap のプロジェクトをやろうと思いつきました。

自分が個人的にぼちぼちやるので、別になにかあるというわけではありませんが、とりあえず表明ということで書いておきます。ひとまず、次の2つを考えています。

1. 反人種差別のためのマッピング
2. 敵対的建築物(Hostile architecture)を記録するマッピング

1. 反人種差別のためのマッピング

OpenStreetMaps USで、社会的公平性のために人種差別に関する地物をマッピングして、wikidataとリンクさせて可視化するというプロジェクトがあります。

• United States/Racism - OpenStreetMap Wiki

アメリカでは、黒人奴隷や南軍、KKK関連の地物をマッピングしています。日本では、戦前、戦中に韓国や中国から強制労働で多数の人が連れて来られて、炭鉱や建設などに従事させられました。

その労働は過酷で、命を落とす人もいたので慰霊のための慰霊碑が建てられていたりします。たとえばこれとか。

• 神戸電鉄朝鮮人労働者の像 - Wikipedia

こういうものを記録していきます。群馬の森の朝鮮人慰霊碑が歴史修正主義者のクレームにより撤去されるという事態も起こっているので、記録は残さないとと思っています。

• 「群馬の森」朝鮮人追悼碑の撤去…平和を願う関係者の無念を映画化 - RKB毎日放送

2. 敵対的建築物(Hostile architecture)を記録するマッピング

敵対的建築物は直訳ですが、日本語では「意地悪ベンチ」「排除アート」と呼ばれているものです。

排除アートは、行政がホームレスや若者がたむろさせないために、ベンチを座りにくくしたり寝られないように手すりをつけることや、人が溜まりやすいスペースに意味不明なアートっぽい（アートではない）オブジェを置いて、そこに滞留できないようにすることです。

• 〈人を座らせないベンチ〉東京・新宿の“意地悪ベンチ”に批判続出！ 一方、下町では真逆の試み…ベンチだらけの商店街を取材 - 集英社オンライン - ニュースを本気で噛み砕け
• RedditのHostile Architectureサブレ
• 排除アート - Wikipedia

ふと気になってOSMのタグを調べたら、そのものズバリ「Hostile architecture」のタグがproposalに出てたので、この動きを推進するために、これらをマッピングします。

• Hostile architecture - OpenStreetMap Wiki
• Proposal:Hostile Architecture - OpenStreetMap Wiki

redditの写真を見れば排除アートがどんなものかわかりますが、広がっている問題については可視化しないとわからない人がいるので、どんどんつけて可視化したいと思ってます。

ということで、時間ができたときに自分はぼちぼちやっていきます。興味がある人がいれば連絡をください。

• https://tools.geofabrik.de/
• https://hdyc.neis-one.org/
• https://osmose.openstreetmap.fr/

包含建筑物、森林。之前陆陆续续修复了一些，不过都是游击式地修复，没有系统地记录过。现在有时间捡起这件事了，先在这里留个坑吧。

……不要给房子加layer标签来逃避冲突检查器的检查。

26/2/21

Taking a break for 1 week because of ramadhan and installing gentoo as my main system.

I have a large set of photographs I made while running. They are geotagged, as I took them with my phone camera. The compass direction is completely unreliable, but lat/lon is more trustworthy. I thought it would be an interesting experiment to extract greenery like grass and trees from these photographs. It can be a useful addition for creating routes that are more pleasant to walk, since the eye-level point of view is not available in OSM. As this is based on my personal photographs, it has the additional benefit of recommending routes that I tend to use. The first challenge I encountered is that out of a few thousand photographs, only a handful were taken during the daytime. After deduplicating and dropping all photos that contain no greenery, this becomes a relatively small set of waypoints. I decided not to extrapolate additional points along OSM ways to keep the dataset small and avoid adding misleading info. The greenery detection works well enough with the SegFormer model, although it is somewhat slow locally. My plan is to select waypoints from this dataset before calling OSRM. This way I get routes that are more enjoyable to walk and run, but are generally longer than the default shortest route. You can find my dataset on Kaggle.

A few quick notes on some changes I made to OSM based on local knowledge.

1. Changed the point for the Riverside Centre building to reflect that it is now a Builder’s Corner hardware store.

2. Added a point for the nearby Hole in the Wall Centre

3. Defined an area for the Somerset Lofts apartment complex and added some details for it.

I’ve recently begun contributing street-level imagery on Mapillary and Panoramax in my local area. I figured that my dash cam was already recording anyway, so if it could be of use to anyone, why not share it?

Contributing to Mapillary was very easy; since my dash cam has an integrated GPS that encoded its data into the video file, I could just upload the video to Mapillary and their website would turn it into an image sequence. Panoramax requires you to preprocess the video into geotagged images yourself, which made it hard to contribute to. Some cameras can be configured to save periodic images instead of videos, but that didn’t work for me because I still needed the dash cam to work normally as a dash cam first and Panoramax instrument second. It took me a while to figure it out, so I’m writing this blog post to hopefully help out the next guy in the same situation.

The task involves four basic steps. I scripted a solution that works specifically for my dash cam model (Garmin 47) and operating system (Linux). If Panoramax continues to grow, I imagine that separate scripts could be written for each step to mix and match for different camera types and computing environments. The steps are:

1. Extract the raw GPS data from the dash cam video clip(s)

2. Along the GPS trace, create a set of evenly-spaced points

3. Extract images from the video occurring at the evenly-spaced points, and

4. Add the GPS and time data to the image files

One could go even further and automatically upload the images to Panoramax straight from the terminal, but that’s beyond my coding abilities.

Let’s take a look at each step in detail:

Step 1 - Getting GPS data from the video

Thankfully, Garmin makes this relatively easy to do with exiftool. If you open the terminal in the directory with the video clips and run the command

exiftool GRMN<number>.MP4

The output will contain a warning:

… See full entry

• I spent some time today improving the map data in my local area using the iD editor. As a local, I noticed that several roads were untracted

• added roads but i got confused while selecting presets- then i realised the more i do mapping, the better i will get with using presets. Each preset serves a unique purpose.

• Few weeks ago i spent time mapping my school in my city, i was soo fun- just wish they could use more updated satelite image.

There has been a very interesting question on the OSM US Slack lately.

“Does anyone have a method to search through the OSM database for a building of a particular shape? I need assistance finding OSM buildings with this specific shape. They should be located in NJ, DE, northeastern MD, eastern PA, or southern NY.”

The question quickly exploded into a huge discussion. At the time of writing, there are already 71 replies.

Someone suggested :

“You could load OSM buildings into PostGIS and then use ST_HausdorffDistance to compare the geometries.”

From there, the discussion veered into how to solve that specific puzzle and find the exact OSM building in question.

One person added, “So the strategy is: create the shape of the building you want to search for, scale it to, say, fill a 100x100 m bounding box or something. Ask Postgres to, within a search-area bounding box, take each building and scale it to a 100x100 m bounding box, compute the Hausdorff distance with the scaled input shape, and return all OSM element IDs and their Hausdorff distances, sorted in ascending order.”

Another said, “What I’m currently doing is combining several shape exports into a single file with around 20,000 objects that have concavity. Concavity plus more than 10 nodes eliminates most buildings.”

---

At that point, instead of hunting that elusive specific OSM building, I became more interested in the generalized version of the problem.

So I added my two cents to the discussion:

“The generalized version of this problem would be : Can we represent a shape in some kind of data type that allows us to computationally check whether two objects have the same shape, regardless of rotation and scaling?

I haven’t studied the Hausdorff distance yet, but I’m wondering whether it can solve this problem, or if there’s a better alternative—Hu moments, Procrustes analysis, Fourier descriptors for contours…”

Someone replied :

… See full entry

New CNEFE Tool Revolutionizes Street Name Correction in OpenStreetMap Brazil

The community of Brazilian mappers has just gained a powerful ally to improve one of the most crucial and, at the same time, challenging data points in any map: street names. The CNEFE Verification System platform has been launched, accessible at https://cnefe.mapaslivre.com.br, a tool created by and for the OpenStreetMap (OSM) community in Brazil, aimed at validating and correcting address data using the latest information from the 2022 IBGE Census.

The project is an initiative of UMBRAOSM (Union of Brazilian OpenStreetMap Mappers) and was developed by experienced mappers Raphael de Assis, president of UMBRAOSM and member of the OpenStreetMap Foundation, and Anderson Toniazo, both active members of the OSM Brazil community. The tool arrives to solve a long-standing bottleneck in national mapping: the updating and verification of street names based on official sources. The Challenge of Street Names in Brazil

For those mapping in Brazil, one of the biggest challenges has always been the lack of a complete, accurate, and freely accessible street database. Through the Demographic Census, IBGE compiles the National Registry of Addresses for Statistical Purposes (CNEFE) . This registry is a vast list of addresses from across the country, containing street names, address types, neighborhoods, and, in many cases, geographic coordinates, especially in rural and non-residential areas.

Historically, the OSM community has used CNEFE data from previous censuses (such as 2010) to enrich the map. However, the process was complex, involving downloading text files (fixed format), cross-referencing them with census tract shapefiles, and extensive manual work to match the information with the streets already drawn on the map, in addition to correcting spelling differences.

… See full entry

Nova Ferramenta CNEFE Revoluciona a Correção de Nomes de Ruas no OpenStreetMap Brasil

A comunidade de mapeadores brasileiros acaba de ganhar uma poderosa aliada para aprimorar um dos dados mais cruciais e, ao mesmo tempo, desafiadores de qualquer mapa: os nomes das ruas. Foi lançada a plataforma Sistema de Verificação CNEFE, acessível em https://cnefe.mapaslivre.com.br, uma ferramenta criada por e para a comunidade OpenStreetMap (OSM) no Brasil, com o objetivo de validar e corrigir os dados de logradouros utilizando as informações mais recentes do Censo 2022 do IBGE.

O projeto é uma iniciativa da UMBRAOSM (União dos Mapeadores Brasileiros do OpenStreetMap) e foi desenvolvido pelos experientes mapeadores Raphael de Assis, presidente da UMBRAOSM e membro da Fundação OpenStreetMap, e Anderson Toniazo, ambos membros ativos da comunidade OSM Brasil. A ferramenta chega para resolver um antigo gargalo no mapeamento nacional: a atualização e verificação dos nomes das ruas a partir de fontes oficiais . O Desafio dos Nomes de Ruas no Brasil

Para quem mapeia no Brasil, um dos grandes desafios sempre foi a falta de uma base de dados de logradouros completa, precisa e de livre acesso. O IBGE, através do Censo Demográfico, coleta o Cadastro Nacional de Endereços para Fins Estatísticos (CNEFE). Este cadastro é uma vasta lista de endereços de todo o país, contendo nomes de ruas, tipos de logradouro, bairros e, em muitos casos, coordenadas geográficas, especialmente em áreas rurais e não residenciais .

Historicamente, a comunidade OSM já utilizava dados do CNEFE de censos anteriores (como o de 2010) para enriquecer o mapa. No entanto, o processo era complexo, envolvendo o download de arquivos de texto (formato fixo), o cruzamento com shapefiles de setores censitários e um trabalho manual intenso para casar as informações com as ruas já desenhadas no mapa, além de corrigir diferenças de grafia .

… See full entry