Comecei a contribuir com o OpenStreetMap movido pela curiosidade e pela vontade de ajudar. Aos poucos, percebi que mapear não era apenas desenhar ruas ou ajustar pontos no mapa: era participar de um projeto global que transforma informação em impacto real.

Nos últimos meses, tenho dedicado boa parte do meu tempo ao mapeamento do Vale do Rio Pardo, no Estado da Bahia. É uma região rica, diversa e ainda pouco representada nos mapas digitais. Cada nova via identificada, cada edificação revisada e cada detalhe adicionado ajuda a tornar o território mais visível e acessível para quem depende dessas informações.

Antes disso, concentrei meus esforços na melhoria do mapa de vários municípios do Sul e Sudoeste Baiano. Foram horas revisando imagens, corrigindo traçados, adicionando estradas rurais e atualizando áreas urbanas. Ver essas regiões ganhando mais precisão e completude no OSM é uma sensação de realização difícil de descrever.

Meu trabalho voluntário envolve revisar imagens de satélite, conferir trilhas, ruas e edificações, além de adicionar detalhes que fazem diferença no dia a dia de quem usa o mapa — desde ciclistas procurando rotas seguras até equipes de resposta a emergências que dependem de dados atualizados. Cada edição que faço carrega um pouco da minha atenção e do meu cuidado, porque sei que alguém, em algum lugar, vai se beneficiar daquela informação.

Também aprendi que mapear é um exercício de paciência e responsabilidade. Às vezes passo longos minutos analisando uma área para garantir que estou representando o mundo real com precisão. Outras vezes, descubro lugares que nunca visitei, mas que passam a fazer parte da minha rotina de voluntário.

O mais gratificante é perceber que, mesmo sem sair de casa, posso contribuir para que comunidades inteiras tenham acesso a mapas melhores. O OpenStreetMap me mostrou que colaboração aberta é poderosa: cada pessoa adiciona um pequeno pedaço, e juntos construímos algo muito maior.

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[Ex-YU] Naški

##Postavimo Banju Luku na (digitalnu) kartu – Pridružite se #FOSMBL

Da li ste se ikada zapitali kako možemo najbolje iskoristiti potencijal našeg regiona? Sve počinje sa podacima. Pod hashtagom #FOSMBL (Friends of OpenStreetMap Banja Luka) želimo da sve izmjene na OpenStreetMap-u u Banjoj Luci i okolini učinimo vidljivijim i lakšim za pronalaženje.

Zašto #FOSMBL? Korištenjem ovog hashtaga u komentarima vaših izmjena (changesets), pomažete u stvaranju lokalne zajednice. To nam omogućava da pratimo napredak i bolje se koordinišemo. Ali cilj je mnogo veći od samog mapiranja:

Turizam: Ažurne karte pomažu turistima da pronađu naše znamenitosti, ugostiteljske objekte i staze.

Ekonomija: Precizni podaci su ključni za logistiku, lokalne biznise i ekonomski razvoj.

Vizija: Vrijeme je da razmislimo o formiranju zvanične OSM grupe za Banju Luku i regiju. Zajedno možemo organizovati “mapatone” i unaprijediti kvalitet podataka na viši nivo.

Hajde da zajedno pokrenemo Banju Luku u digitalnom svijetu!

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[DE] Deutsch

##Banja Luka sichtbarer machen – Ein Aufruf an alle Mapper

Hast du dich jemals gefragt, wie wir das volle Potenzial unserer Region ausschöpfen können? Alles beginnt mit Daten. Unter dem Hashtag #FOSMBL (Friends of OpenStreetMap Banja Luka) wollen wir ab sofort alle Änderungen in OpenStreetMap in und um Banja Luka bündeln.

Warum #FOSMBL? Indem wir diesen Hashtag in unseren Changeset-Kommentaren verwenden, machen wir unsere Arbeit sichtbar. Es hilft uns zu sehen, wer woran arbeitet, und Fortschritte gemeinsam zu feiern. Aber es geht um mehr als nur Linien auf einer Karte:

Tourismus: Aktuelle Karten helfen Gästen, unsere Sehenswürdigkeiten, Cafés und Wanderwege zu finden.

Wirtschaft: Eine präzise Infrastruktur ist die Basis für Logistik und lokale Unternehmen.

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Come circoscrivendo l’area interessata, contattare chi ha già effettuato mappature o rilievi? Agosto77

Mir ist aufgefallen, daß die Überwachungskameras, die an jeder U-Bahn-Station im Raum Nürnberg-Fürth hängen, in OSM kaum verzeichnet sind. Ich habe daher angefangen, alle, die mir beim Warten auf die U-Bahn auffallen, einzuzeichnen. Dafür verwende ich Vespucci am Handy mit folgenden Tagsets:

man-made:surveillance
surveillance:public
surveillance-type:camera
surveillance-zone:public_transport_platform
camera-type:#ist entweder fixed oder dome
camera-mount:#ist entweder wall oder ceiling
camera-direction:#oft nur geschätzte Richtwerte
camera-angle:#ich versuche, mit der Android-App Clinometer möglichst akkurate Schätzwerte zum Neigungswinkel zu bekommen, funktioniert bisher erstaunlich gut
height:#nur Schätzungen
level:#selbsterklärend
operator:VAG Verkehrs-Aktiengesellschaft Nürnberg
operator-type:public
operator-wikidata:Q1557682
ref:#jede gerichtete VAG-Kamera hat an der Seite ein gelbes Schild, auf dem eine alphanumerische Referenznummer aufgedruckt ist, nach dem Schema 'VAG-NR. ###', wobei # jeweils eine Ziffer darstellt.

Die Referenznummern scheinen keinem bestimmten Schema zu folgen, jedenfalls nicht, soweit ich das nachvollziehen kann. Außerdem scheinen die dome-type Kameras kein Schild mit einer Referenznummer zu haben.

Wenn ihr Fragen oder Anregungen zu dem Thema habt, schreibt mir gerne, dann können wir ins Gespräch kommen. Fröhliches Kartographieren mittens_unofficial

Devo mappare una strada privata a uso pubblico vicino al Sentiero della Felce, sotto il Parco di Belmonte…ho visto che alcuni nodi, parti del sentiero sono stati mappati da utenti diversi, ma non questa strada carrabile.. Chiedo aiuto per indicazioni a chiunque voglia contribuire. Agosto77

Quantity vs Quality Assurance

I wanted to know how well I was performing as an armchair mapper, internally I was concerned that I was creating more issues than I was helping so I learned about a website that answers the question “How did I contribute?”.

On my name I looked at the quality assurance section and the quantity of errors was too very high, almost over a 4000 issues ignored. I have attempted to improve the quality of my mapping change sets using Osmose and OSMI.

Osmose

Initially I had 26 level 1(major) issues, 50 level 2(intermediate) issues and 800(minor). I quickly adjusted the level 1 issues and over half of my level 2 issues. I have a single level 1, 19 level 2 and over 700 level 3 issues. Systematically I hope to solve; *amenity=parking issues *building=construction issues *building overlap issues *waterway issues.

OSMI

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从宜昌回来半个月后依然怀念肥鱼的味道

“I don’t know, can you?” is a joke of mediocre quality that is supposed to point out the difference between being able to do something and being allowed to do something. It is annoying to students all over because in everyday language, the word “can” gets used universally for both instances, and context clues make such comments seem pedantic. There is, however, a great place where pedantry is more than welcome: When maintaining a database, such as OpenStreetMap.

So, what’s the issue?

Access tags, including more granular specifications like motor_vehicle, bicycle or foot, specify whether someone is allowed to use a way, an amenity or whatever else. The wheelchair tag, on the other hand, specifies if it is possible to access or use the place, independent of whether it is allowed or not. Tag combinations like access=private and wheelchair=yes make perfect sense. Only a limited set of people are allowed to access a certain area, but when they are, they won’t be hindered by stairs, narrow paths or other issues that might make using a wheelchair difficult or impossible. Therefore, the only values that really make sense to use for the wheelchair tag are yes, no, limited, and designated. There are some other, user defined values that might make sense in a specific setting, but it does not make sense to apply the access tag logic to wheelchair tagging. Tag values like private, permissive and customers are an established way to convey access permission information, which is not what the wheelchair tag is.

To underline the difference between access tags and wheelchair tags, you can look at the iD editors input mask and see that they are completely different segments.

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wiki.openstreetmap.org/wiki/RU:Россия/Соглашение_об_именовании_дорог

https://hdyc.neis-one.org/

ここでいう鉄道路線リレーションとは、route=railway（例えば、東海道本線、片町線）であり、列車の運行系統を示すroute=train（例えば、JR神戸線、湘南新宿ライン）とは違う。

修正進捗

高山本線（2026/01/06 済）

東海道本線（2026/01/10 済）

山陽本線（進行中）

東北本線

（ほか）

First and foremost, I want to express my sincere gratitude to the ESA Hub organization for selecting me for this fellowship. It was an honor to be chosen, and I am thankful for the opportunity to learn and contribute. When I began the program, I must admit I lacked confidence in the validation process. While I understood the basics of OpenStreetMap (OSM), the responsibility of critiquing and correcting other mappers’ work felt daunting. I often second-guessed my ability to distinguish between a mapping error and a local anomaly. However, looking back now, this fellowship has been a deeply enriching and practical experience that completely transformed that hesitation into technical authority.

The program didn’t just teach me how to map; it provided a robust understanding of the OSM ecosystem and the HOT Tasking Manager workflows. Through hands-on practice, I moved from simple digitization to mastering advanced tools in JOSM, including plugins, filters, search functions, and custom map paint styles.

A significant part of my growth came from the specific tips and tricks shared by my peers and mentors. I am especially grateful to Brenda, who taught me the ingenious technique of turning satellite imagery to black and white to better distinguish building outlines from the surrounding terrain. Kingsley was also instrumental, introducing us to various keyboard shortcuts that have significantly enhanced the speed and precision of my mapping and validation. Furthermore, I learned about features I didn’t even know existed, such as a ford (a shallow place in a river allowing a crossing). Learning how to identify and tag such specific infrastructure made me realize that high-quality mapping is about more than just drawing shapes; it’s about capturing the reality of the ground to aid responders.

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My experience in the 2025 Validator Fellowship for Eastern and Southern Africa began on November 3rd, 2025, and concluded on January 7th, 2026, with all sessions held remotely. The fellowship consisted of 12 countries across Eastern and Southern Africa with 42 fellows. https://www.linkedin.com/pulse/introducing-2025-esa-validator-fellowship-z0nifutm_source=share&utm_medium=member_android&utm_campaign=share_via The program kicked off with an introduction to the Java OpenStreetMap Editor (JOSM), which was followed by an in-depth examination of its advanced features. This phase was particularly enlightening, as I discovered various validation tools that could significantly enhance the validation process. Before we could dive in the fellowship one had to complete a learning lesson. https://learning.hotosm.org/course/josm-skills-series

1. Introduction JOSM

https://docs.google.com/presentation/d/1YjRJpVQZ9BnY7xmHOswq1DDvJq5b3Id2YsEs81QWVt0/edit?usp=sharing

-JOSM training i.e mapping and validation using JOSM; task #19146, #34096

Reflecting on my earlier encounters with validation, I remember a time when my approach was rather simplistic; I would merely activate the validation tool, rectify minor errors, and quickly mark tasks as validated. My lack of confidence or hesitance to trust the accuracy of my work often deterred me from pursuing further validation, leaving me unaware of the more effective techniques that were at my disposal.

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Monatliche OSM-Kolumne, Januar 2026

OSM an der Infrastruktur-Schwelle

OpenStreetMap steht 2026 an einem Wendepunkt, der weniger dramatisch wirkt als er faktisch ist. Die Zahlen sprechen eine klare Sprache: Der Myanmar-Erdbeben-Response im März 2025 mobilisierte 3.296 Volunteer-Mapper, um 1.438.900 Gebäude und 17.603 km Straßen zu kartographieren – ein logistisches Wunder. Gleichzeitig offenbaren diese Zahlen eine unbequeme Wahrheit: OpenStreetMap ist nicht mehr ein nischenhaftes Wiki-Projekt, sondern kritische digitale Infrastruktur, die von der Bahn, DHL, Meta, Microsoft bis zum Roten Kreuz in Echtzeit genutzt wird.

Doch die Infrastruktur selbst – das Code-Gerüst, das diese Kartographie überhaupt möglich macht – zeigt Verschleißerscheinungen. Die Sovereign Tech Agency erkannte dies im Dezember 2024 und investierte 384.000 EUR über zwei Jahre, um OpenStreetMaps Kerninfrastruktur zu modernisieren. Diese Kolumne folgt den Fäden, die sich von diesem Investment bis zur State of the Map 2026 in Paris ziehen – und zeigt, warum diese Fäden in Wien beginnen, wo Fragen von Zugänglichkeit, Gerechtigkeit und Community-Partizipation nicht abstrakt sind.

Teil 1: Governance-Krise und die Resilienz-Frage

Das Defizit: Wer wartet OSMs Herz? Eine unbequeme Tatsache: OpenStreetMaps Kerninfrastruktur wird von einer Handvoll Entwickler unterhalten. Nicht Dutzend – Handvoll. Für kritische Projekte wie die Datenbank, die API und das Website-Frontend gibt es oft nur eine oder zwei Personen. Das ist nicht Schlankheit – das ist Single-Point-of-Failure-Architektur.

Die Operations Working Group (OWG) der OpenStreetMap Foundation hat zwar Oversightfunktionen, doch die Entscheidungswege sind:

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Árboles urbanos de la ciudad de Pitrufquén en OSM, 2025.

Recientemente la ciudad de Pitrufquén ubicada en la comuna homónima (Región de La Araucanía. Chile), a través de la Municipalidad solicito la elaboración de un catastro del arbolado urbano en el sector centro de la ciudad. Comprendiendo el polígono formado por las calles desde General Baquedano hasta O’higgins en sentido Este-Oeste y desde Domingo Santa María hasta Caupolicán en el sentido Norte-Sur. La superficie del polígono a mapear es de 80 hectáreas, con urgencia de ejecución de diez días entre el 19 al 29 de diciembre, antes de cierre de año. Dada las limitaciones de tiempo y presupuestarias para realizar un catastro en terreno como corresponde, se optó por la realización del levantamiento de forma virtual en la plataforma de OSM.

Se deben registrar e identificar las especies, estado sanitario, el numero de árboles al interior del polígono. Las variables que se registraron para natural=tree son:

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Flood Risk Map of Kenya using GIS

For the doc version: Kenya Flood Risk Map

Abstract

The Republic of Kenya has recently witnessed a series of devastating hydrometeorological events, transitioning from a severe multi-year drought to catastrophic, El Niño-enhanced flooding between 2024 and 2025. These events have underscored a critical need for high-resolution spatial data to inform disaster risk reduction and humanitarian response. This research, produced as a Legacy Project for the Humanitarian OpenStreetMap Team (HOT) Community Working Group (CWG) Mentorship 2025, presents a comprehensive national-scale flood risk assessment for Kenya. The study employs a Geographic Information System (GIS) and Multi-Criteria Decision Analysis (MCDA) framework to synthesize six influential factors: rainfall intensity, elevation, slope, Land Use/Land Cover (LULC), distance to water bodies, and distance to road networks. Utilizing a weighted overlay methodology, the study reclassifies these parameters based on their hydrological and anthropogenic influence to produce a final flood risk map categorized into five classes: Very High, High, Moderate, Low, and Very Low. The analysis reveals that high-risk zones are predominantly concentrated in low-lying river basins and informal urban settlements, where high rainfall accumulation coincides with poor drainage and high exposure. The findings provide a strategic foundation for the OpenStreetMap community and disaster management agencies to prioritize anticipatory actions, refine field data collection, and enhance the resilience of vulnerable populations.

Keywords

flood, Kenya, flood risk, mapping, GIS, Multi-Criteria Decision Analysis, OpenStreetMap

Introduction

The Kenyan Paradox: Historical Context and Emerging Flood Dynamics

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It’s a pretty nice jump.

On November 4th, 2025, I noticed that there were 9,566 buildings mapped in Delaware County, and told a few friends that I wanted to push it up to 10,000. Since then, I’ve been mapping buildings in Delaware County daily, averaging over 100 buildings per day. Once I got it past 10,000, my next goal was to get it so that building mapping in 2025 would outpace building construction in the county. Delaware County is a rapidly growing county, so I did some napkin math and guessed that there were about 1500 buildings added to the county in the year. By the end of the year, I had easily surpassed that goal, and was now working to push back that date of keeping pace with construction further and further. You can see that in this graph:

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​I moved to Caracaraí, Roraima, for work (the banking life) in the first half of 2025. It is a quiet town.

​By the end of the year, bothered by seeing that the city’s map on OpenStreetMap consisted merely of outdated streets, the City Hall, and the hospital, I decided to join OSM on December 29th.

​It is an Amazonian town of 20,000 inhabitants. The result of this first week is over 300 changesets trying to pull the city out of the void.

​All public amenities (that I can recall), such as schools, health centers, banks, etc., are mapped. And so far, half of the city’s buildings are already drawn (long live the Building Tool plugin!).

​Honestly, I don’t know who will care about detailed mapping here in the middle of nowhere, but I wanted to do it anyway.

Me mudei a trabalho (vida de bancário) para a cidade de Caracaraí, em Roraima no primeiro semestre de 2025. Cidade pacata.

Já no final do ano, incomodado em ver que mapa da cidade no OpenStreetMap se resumia a ruas desatualizadas, a prefeitura e o hospital, decidi entrar no OSM, nesse último 29 de dezembro.

​É uma cidade amazônica de 20 mil habitantes. ​O resultado dessa primeira semana são mais de 300 changesets tentando tirar a cidade do vazio.

Todos (que eu me lembre) as funções públicas como escolas, postos de saúde, bancos etc estão feitas. E até o momento metade das edificações da cidade já desenhadas (viva o plugin Building Tool).

Sinceramente, não sei a quem vai importar um mapeamento detalhado aqui no meio do nada, mas eu quis fazer assim mesmo.

I find that the width of OSM ways is a useful property for determining how good a pedestrian route is. However, it is often missing from OSM. As an experiment, I decided to use my running activities from Strava to estimate the width of a single OSM way that I use often. The specific way ID I used is in a relatively open area, meaning GNSS error is minimized. I also have collected over 100 traces of me running that single way ID over ~1.5 years. Given all this, how accurate can the estimate of the width be? I got the median width to be in the range of 11 meters. The actual width as measured with Google Maps satellite imagery is 13 meters. It’s close. I am happy with the result. I don’t have nearly as many traces for any other segment on the OSM map, so it’s a limited experiment, but the potential is promising. See the code on Github.

“From here, how do we get to Ragunan Zoo?”

Good question.

I paused. This wasn’t a matter of intuition; it was a routing problem.

I opened a navigation app, queried the destination, and switched the mode to public transport. The proposed solution was a multi-hop journey : take the blue commuter line to Manggarai, transfer to the red line toward Bogor, get off at Pasar Minggu, then continue with something called S15A.

S15A?

That identifier triggered a red flag. After a quick lookup, it turned out to be an angkot.

That immediately raised another question. Was there really no direct busway route to Ragunan? Not even a JakLingko alternative? Cost sensitivity was also a concern. There are plenty of public transportation modes in this city: MRT, LRT, Commuter Line, Transjakarta BRT, and Transjakarta non-BRT, but angkot and ride-hailing motorcycles are the two worst options, since they can end up being pricey due to the lack of government subsidization.

At that point, I decided to discard the initial navigation output entirely. Close the app. Start over with a more specialized tool.

I switched to the official Transjakarta application.

It refused to open and forced an update. Fine. Update first, then rerun the query.

Post-update, I defined the problem more explicitly. Assume the train leg was already completed. Starting point: Pasar Minggu Station. Destination: Ragunan. The goal was to find a replacement for the S15A angkot.

Search results came back clean. Instead of S15A, there was a JakLingko option: JAK47, Pasar Minggu–Ragunan. That was acceptable. Same endpoint, better integration.

Solution candidate number one locked in.

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Then I expanded the scope. What if we removed the train entirely? What if the journey started directly from Bekasi using Transjakarta, via Vida to Cawang Sentral?

New query. New parameters. Starting point: Cawang Sentral. Destination: Ragunan.

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