Pokus

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、https://wiki.openstreetmap.org/wiki/Tag: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https://wiki.openstreetmap.org/wiki/Tag: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.

None of this would have been possible without the support structure of the fellowship. A special thanks goes to our moderator, Rebecca Chandiru, who ensured we always had access to session recordings so we never fell behind. Her constant encouragement pushed me to overcome my initial fears and step up to validate with confidence.

Reflecting on this journey, the ESA Hub Fellowship has been a milestone in my professional growth, bridging the gap between technical theory and real-world humanitarian impact.

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.

As I delved deeper into the training, I learned about essential tools such as Map Paint styles and JOSM plugins, ( https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://www.youtube.com/watch%3Fv%3DYZ2vFbGItWU&ved=2ahUKEwjWzoeM4v6RAxUgWUEAHSy-EyAQwqsBegQIFBAB&usg=AOvVaw0VXKSVry_BHx1hQfeYkytt) which streamline the validation process and facilitate the identification and correction of errors with greater efficiency. One of the most valuable shortcuts I acquired was the ability to square multiple buildings simultaneously, rather than addressing each one individually. The use of short cuts (ctrl f ) to query for example buildings which are wrongly tagged and you can select all of them then rectify the mistake at once. This realization was especially impactful, as it brought to mind a previous instance when I received feedback regarding my squaring of buildings. Initially, I reacted defensively, convinced that I had adequately squared all the structures. However, this experience ultimately illuminated the mistakes I had been making and underscored the necessity for ongoing learning and improvement in my validation practices.

One of the most fulfilling aspects of the fellowship was the strong emphasis on peer-to-peer engagement. This collaborative environment encouraged participants to share insights, discuss challenges, and exchange best practices. By learning from one another, we not only enriched our individual experiences but also fostered a supportive community that promoted growth and innovation. During my fellowship, I vividly recall participating in the Japan task, which involved mapping and validating damaged buildings. This experience required us to utilize imagery that was different from the standard resources we typically employed, along with adhering to specific tagging protocols. Initially, I felt a lack of confidence in my ability to contribute effectively to this task. However, after voicing my concerns within the group, I found that my apprehensions were shared by others, which fostered a collaborative environment. This was one of my first experiences with mapping in a structured context, and I am grateful for the opportunity to learn and grow through it. The task not only enhanced my technical skills but also reinforced the importance of communication and teamwork in overcoming challenges. The open exchange of ideas and constructive feedback among peers proved to be invaluable, as it allowed me to refine my methodologies and deepen my understanding of the validation landscape. This collective learning experience not only enhanced my skills but also strengthened my commitment to the goals of the fellowship.

Throughout the fellowship, I also gained insights into the third-pass validation process, culminating in data export, usage, and map creation.

https://drive.google.com/file/d/16AC5cssasa9bO2FH_ZvFgJkLnlPTJV5o/view?usp=sharing

While I have experienced many successes, I recognize that I have a limitation when it comes to managing multiple tasks simultaneously validating. This is an area where I am actively working to improve, and I am confident that with continued effort and practice, I will enhance my ability to juggle various responsibilities effectively. I believe that with time and dedication, I will reach a level of proficiency that allows me to handle multiple tasks with greater ease and efficiency. Well the essential focus should not be on the sheer volume of work completed, but rather on the quality of the output produced. While I may have made significant strides in the number of buildings and roads that I have mapped and edited and validated, the critical question remains whether the work has been executed to a high standard. It is imperative to assess not just the quantity of projects undertaken, but also the thoroughness and precision with which each task has been accomplished. Quality assurance in mapping and editing is vital, as it ultimately determines the effectiveness and usability of the work in real-world applications.

I am now equipped with a set of skills that I feel compelled to share with my community. The knowledge I have acquired not only enhances my own capabilities but also empowers me to contribute to the collective growth of others in the field. I believe that by disseminating this information, I can help foster a more knowledgeable and skilled community, ultimately leading to improved outcomes in our validation efforts. Thank you my team 🤗🙏

OpenStreetMap steht an einem kritischen Wendepunkt. Während die globale Community die Vorbereitungen für die State of the Map Konferenz in Paris (28.–30. August 2026) vorantreibt, offenbaren sich tiefgreifende Fragen zu Governance, Datenqualität und der Frage, für wen wir eigentlich kartographieren. Diese Wochenkolumne beleuchtet aktuelle Entwicklungen, lokale Initiativen aus Wien und globale Herausforderungen, die unsere Arbeit als digitale Kartographen prägen.

Governance und Machtstrukturen im offenen Raum: Wer entscheidet, was OSM ist? Die Frage der Governance war 2025 ein Brennpunkt innerhalb der OSM-Community. Dabei geht es nicht nur um technische Entscheidungen, sondern um fundamentale Fragen: Wer bestimmt, welche Software zur Kerninfrastruktur von OpenStreetMap gehört? Wer hat Einsicht in die Entscheidungsprozesse? Und wie werden neuere Beiträger in diese Diskurse eingebunden? ​

Aktuell zeigt sich ein erhebliches Governance-Defizit. Die Kernsoftware von OpenStreetMap wird von einer kleinen Gruppe von Entwicklern gepflegt – oft nur einer oder zwei Personen pro kritischem Projekt. Das Operations Working Group (OWG) der OpenStreetMap Foundation hat zwar Oversight-Funktionen, doch sind die Entscheidungswege intransparent und häufig informell strukturiert. Dies steht im krassen Gegensatz zu anderen OSM-Institutionen wie der Licensing Working Group oder dem Tagging-Gremium, die deutlich formalisiertere Prozesse haben. ​

Die Sovereign Tech Agency hat erkannt, dass dies ein erhebliches Risiko für die Langzeitstabilität des Ökosystems darstellt. Im Dezember 2025 kündigte sie daher erhebliche Investitionen in die Modernisierung der OSM-Kerninfrastruktur an. Dies umfasst nicht nur Code-Refactoring, sondern auch Dokumentation, verbesserte Entwickler-Onboarding-Prozesse und Sukzessionspläne für langjährige Maintainer. Ziel ist es, die Entwickler-Community zu diversifizieren und damit die langfristige Resilienz des Projekts zu sichern. ​

Besonders bemerkenswert ist, dass Konflikte von Interesse nicht systematisch adressiert werden. Einige Mitglieder des OWG sind gleichzeitig Maintainer von Kernprojekten – was neutrale Entscheidungsfindung erschwert. Eine echte demokratische Governance für Softwareentscheidungen fehlt bislang völlig. ​

Barrierefreiheit als Datengerechtigkeit: Accessibility-Mapping und die Wheelmap Während Governance-Strukturen oft unsichtbar bleiben, zeigt sich die physische Realität von Ausgrenzung täglich auf den Straßen. Hier spielt barrierefreies Mapping eine zentrale Rolle. Die Wheelmap-Plattform hat sich seit ihrer Gründung 2010 zur weltweit umfassendsten Datenbank für Rollstuhlzugänglichkeit entwickelt – mit über 800.000 gesammelten Orten. ​

Das Konzept ist elegant: Der wheelchair=* Tag in OSM bildet die Grundlage, und Wheelmap bietet eine benutzerfreundliche Schnittstelle für nicht-technische Mapper. Werte wie wheelchair=yes (vollständig zugänglich), wheelchair=limited (teilweise) und wheelchair=no (nicht zugänglich) werden durch spezialisierte Sub-Tags ergänzt: wheelchair:description=* für Notizen, toilets:wheelchair=* für WC-Informationen, oder ramp:wheelchair=yes für Rampendetails. ​

Doch die Arbeit ist keineswegs abgeschlossen. Eine der größten Herausforderungen liegt in der Coverage-Sparsity: Selbst in europäischen Metropolen sind nur wenige Prozent aller Lokale mit Accessibility-Daten versehen. Und hier offenbaren sich wieder Machtfragen: Wer kartographiert für wen? Internationale Mapper dominieren oft Länder des Globalen Südens – ohne echte Partizipation lokaler Communities. Beim Cyclone Idai in Mozambique 2019 zeigten sich diese Asymmetrien deutlich: 90% der Mapper kamen aus dem Ausland, lokale Toponymie und Kontexte wurden vernachlässigt. ​

Das Projekt Accessible Maps der Technischen Universität Dresden und des Karlsruher Instituts für Technologie versucht diese Lücke mit einem innovativen Ansatz zu schließen: Sie entwickeln Tools zur automatisierten Erfassung von Indoor-Barrierefreiheit aus Gebäudeplänen, kombiniert mit Crowdsourcing-Elementen. Gleichzeitig standardisieren sie über 800 Barrierefreiheitsmerkmale in Kooperation mit Sozialhelden e.V., um konsistente Tagging-Konventionen zu schaffen. ​

Für Mapper in Wien bedeutet dies konkret: Das Nordbahnviertel und die Leopoldstadt sind ideale Testfelder für solche partizipativen Projekte. Die neuen Strukturen im Nordbahnviertel bieten eine Gelegenheit, Accessibility von Anfang an einzuplanen – nicht als Nachtrag, sondern als integrativer Bestandteil der Kartographie.

Micro-Mapping in Wien: Kleinstrukturen als Infrastruktur der Gerechtigkeit Micro-Mapping – die hochdetaillierte Kartographie von Kleinstrukturen – ist mehr als nur kartographische Spielerei. Sie ist ein Werkzeug, um die tatsächliche Nutzbarkeit von öffentlichem Raum abzubilden. ​

In der Leopoldstadt und dem Nordbahnviertel finden sich ideale Anwendungsszenarien. Welche Tags gehören zur fundamentalen Micro-Mapping-Infrastruktur? ​

highway=street_lamp – Straßenlaternen prägen die nächtliche Sicherheit und Orientierung

leisure=outdoor_seating – Sitzmöglichkeiten sind zentral für ältere Menschen und Eltern mit Kinderwagen

amenity=bench – Bänke alle 100–150 Meter sind ein Inklusionsfaktor

entrance=* – detaillierte Eingänge ermöglichen barrierefreies Routing

highway=crossing + crossing=traffic_signals – Zebrastreifen mit Ampeln sind Leben und Tod für blinde Menschen

wheelchair=* auf Wegen und Plätzen – Zugänglichkeit als primäre Dimension

Diese Tags sind nicht akademisch. Sie werden unmittelbar von StreetComplete und Every Door genutzt – zwei Editoren, die 2024 einen explosiven Zuwachs verzeichneten. StreetComplete allein erzeugte über 12,9 Millionen Edits im Jahr 2024. Every Door konzentriert sich speziell auf Micro-Mapping und Adressdetails und empfängt 2025 NGI0-Commons-Förderung, um seine Funktionalität auszubauen. ​

Die physische Realität Wiens – dichte historische Bebauung, herausfordernde Topographie, extreme Wetterbedingungen – erfordert genau diese Granularität. Ein Rollstuhlfahrer braucht nicht nur zu wissen, dass die U1-Station Vorgartenstraße „rollstuhlgerecht” ist, sondern wo konkret die Rampe beginnt, welche Breite sie hat, ob es eine Toilette mit Euroschlüssel gibt. ​

Humanitäre Kartographie und die Frage der Datenqualität Das Humanitarian OpenStreetMap Team (HOT) hat 2025 ein Dilemma offenbart, das das gesamte Ökosystem betrifft: Wie skaliert man qualitativ hochwertige Kartographie in Krisen? Die Antwort ist unbequem.

Der Validierungsprozess in HOT-Projekten erfolgt typischerweise in vier Schritten: (1) Mapping von Grundobjekten, (2) Validierung auf Vollständigkeit, (3) Invalidierung fehlerhafter Tasks mit Feedback, (4) „Final clean-up” zur Fehlerbereinigung an Grenzflächen. Dies funktioniert – aber es ist bottleneck-anfällig. Validation ist ein knappes Gut. Eine Analyse von 746 abgeschlossenen HOT-Tasking-Manager-Projekten zeigte klar: Das Verhältnis von Mapping zu Validation ist asymmetrisch, mit häufig 60% Mapping und nur 40% Validation. ​

Besonders problematisch wird dies in Kontexten hoher Datenarmut. Im Jakande Housing Estate in Lagos, Nigeria, zeigten sich über 40% der Wohnflächen in Hochwasser-Hochrisikogebieten – doch ohne OSM-Daten wären diese gefährlichen Räume unsichtbar geblieben. HOT mobilisierte über 8.000 Projekte in 150 Ländern, um solche Lücken zu schließen. Doch die Qualität ist variabel: In südafrikanischen Projekten dominieren internationale Mapper, was zu Genauigkeitsverlust und fehlender lokaler Kontextualisierung führt. ​

Was hilft? Lokalisierung von Validator-Teams. Das American Red Cross startete 2020 ein Validator-Trainings-Programm mit 30 neuen Voluntären und erzeugte damit messbare Qualitätsgewinne. Dieser Ansatz – Lokalisierung, systematisches Training, Peer-Support – ist kostengünstiger und nachhaltiger als externe Validation. ​

Für die Wiener OSM-Community bedeutet dies: Wir haben lokales Expertenwissen, das exportierbar ist. Das Stammtisch-Netzwerk der OSM-AT könnte Trainings-Expertise in Schwellenländer bringen – nicht als imperiale Geste, sondern als echte Kapazitätsbildung.

Digitales Erbe und Resilienz: OSM-Daten als Archiv der Zukunft Die OSHDB (OpenStreetMap History Database) und das ohsome API sind stille Revolutionäre. Sie ermöglichen es, die gesamte Kartographiegeschichte seit 2007 zu durchsuchen – mit granularer zeitlicher und räumlicher Auflösung. Das Dashboard-Tool erlaubt es jedem, etwa für das Nordbahnviertel eine Frage zu stellen: Wann wurde diese Fläche kartographiert? Wie hat sich ihre Beschreibung verändert? Die Antwort erscheint in Sekunden. ​

Warum ist das relevant für ein digitales Erbe? Weil OSM-Daten eine Zeugnis-Funktion haben. Sie dokumentieren, wie die Welt zu verschiedenen Zeiten wahrgenommen wurde – oder eben nicht. Ein Forschungsteam in Tanzania nutzte diese Funktion, um zu zeigen, dass hunderte von Siedlungen historisch „unsichtbar” waren für offizielle Geodaten. Die OSM-Community machte sie sichtbar. Das ist nicht nur Kartographie – das ist Archive justice. ​

Gleichzeitig zeigt OSHDB-Analyse, dass Krisenphasen Kartographie-Explosionen auslösen: Nach Naturkatastrophen springen die Änderungsraten an. Diese Daten sind kritisch für Disaster Risk Reduction. Das Deutsche Rote Kreuz nutzt HOT-Daten explizit für Frühwarnkartographie. ​

Doch es gibt ein Risiko: Ohne bewusste Archivierungspraktiken könnte OSM-Geschichte verloren gehen. Der Sovereign Tech Fund investiert deshalb in längerfristige Datenspeicherung und -analyse-Infrastruktur. Dies ist nicht romantisch – es ist resilience engineering. Künftige Generationen sollen verstehen, was wir 2026 über Wien wussten – und was wir übersehen haben. ​

POI-Profiling und Datenqualitäts-Frameworks: Systematische Qualitätskontrolle Ein neuer Trend in der OSM-Forschung ist das strukturierte POI-Profiling. Dabei werden Points of Interest nicht nur kartographiert, sondern klassifiziert nach Qualitätsmetriken: True Positives (echte, aktuelle POIs), False Positives (kartographiert, aber nicht real) und False Negatives (real, aber nicht kartographiert). ​

Eine Analyse für Wohnimmobilien-Standortanalysen in deutschsprachigen Städten zeigte signifikante Qualitätslücken. Ärzte (OSM: 962 vs. TomTom: 26.728) und Zahnärzte (OSM: 387 vs. TomTom: 28.067) sind deutlich unterrepräsentiert – wahrscheinlich weil diese Berufsgruppen weniger in Routingdatenbanken auftauchen, aber sozialgeographisch kritisch sind. ​

Für Wien-Mapper bedeutet das konkret: Wir sollten systematisch Professionen und Dienstleistungen kartographieren, die gesellschaftlich marginal wirken, aber für vulnerablen Gruppen entscheidend sind. Frauenärzte, Migrantenbratungsstellen, Obdachlosenunterkünfte, Harm-Reduction-Zentren für Drogennutzer.

Das Data Quality Approach für Lateinamerika definiert drei automatisierte Validierungswerkzeuge: ​

JOSM Validator (für lokal-konsistente Fehler)

iD Editor Validator (vor dem Upload)

Osmose (für globale Musterfehler: doppelte Nodes, Straßen-Disconnects)

Doch automatische Validator können keine semantische Relevanz erfassen. Ein leerer POI-Tag ist für den Validator korrekt, aber für einen blinden Nutzer wertlos.

Governance-Tagging: Rechtliche und administrative Attributes als normalisiertes Kontextwissen Weniger glamourös, aber zentral: Governance-Tagging. Dies betrifft die administrative Struktur von Raum und Autorität.

Der zentrale Tag ist boundary=administrative mit dem Hierarchie-System admin_level=1–11. In Österreich entspricht dies: ​

admin_level=1 – österreichische Staatsgrenzen

admin_level=2 – (nicht anwendbar; österreichische Ebene)

admin_level=3 – Bundesländer (Wien = Land)

admin_level=4 – Bezirke (Leopoldstadt = 2. Bezirk)

admin_level=7 – Ortschaften/Grätzl (informal in Wien)

Zusätzlich wird government=administrative für Behördensitze verwendet – etwa die Bezirksvorstehung Leopoldstadt. Mit admin_level=* in Kombination, etwa office=government + admin_level=4, wird klar: Das ist eine Bezirksbehörde. ​

Die Rechtliche Implikation ist erheblich: Wenn adminstrative Grenzen falsch sind, sind Routing-Ergebnisse falsch. Eine Analyse von Vienna-Geodaten aus 2012 zeigt, dass noch immer “Doppel-Mapping von geometrisch idententen Verwaltungsgrenzen” in Wien ungelöst ist. Das Nordbahnviertel – straddling the 2. Bezirk – ist ein Testfall für bessere administrative Genauigkeit. ​

Community und lokale Inititiativen: Wiener Stammtische und das österreichische Local Chapter Die OSM Austria wurde 2022 offizielles Local Chapter der OSMF. Das ist mehr als symbolisch. Es bedeutet formale Anerkennung, Zugang zu Finanzierungsressourcen und Mitsprache bei Entscheidungen auf europäischer Ebene. ​

Die virtuellen Stammtische finden alle drei Monate statt und versammeln etwa 20–30 aktive Mapper aus dem deutschsprachigen Raum. Die Themenlandschaft ist vielfältig – von technischen Tagging-Diskussionen (Fahrradquerungen, Wintersperren, Lawinenschranken) bis zu politisch-kartographischen Fragen wie der Nutzung von Raumordnungskonzepten als freie Datenquellen. ​

Besonders bemerkenswert ist die Frauenpräsenz – zwar nicht Mehrheit, aber ein bewusster Fokus auf Diversität. HOT hat global dokumentiert: Frauengeleite Mapping priorisiert Orte, die für Frauensicherheit kritisch sind – gut beleuchtete Wege, öffentliche Toiletten, Frauenzentren, Kinderbetreuung. Dies ist keine Nische; es ist Datenqualität im Sinne von Gerechtigkeit. ​

Für Leopoldstadt konkret: Das Volkert-Grätzel und das Alliiertenviertel sind zunehmend Gentrifizierungsgebiet. OSM-Mapping könnte hier dokumentieren: Wo verschwinden niedrig-kostenfreie Räume? Wo entstehen Schutzräume für vulnerable Gruppen? Das ist nicht nostalgische Kartographie – es ist Advocacy-Arbeit durch Daten.

State of the Map 2026: Paris als Wendepunkt? State of the Map findet 2026 in Paris statt – die dritte Karte Europas nach Kontinentalbeitrag. Das ist symbolisch für einen Moment, in dem OpenStreetMap von der Nische in die Infrastruktur einzieht. Die französische Community ist hochorganisiert, mit etabliertem SotM-France Track seit 2013. ​

Das Programm wird 2026 voraussichtlich folgende Foci haben:

Governance-Reformen: Wie werden transparentere Entscheidungsprozesse etabliert?

AI and VGI: Wie integriert man Street-View-Imagery und AI-Assistenzmapler, ohne OSM’s partizipatives Modell zu untergraben?

Gender and Equity: Wie wird Mapping inklusiver – nicht nur mehr Mapper, sondern Mapper mit marginalisierten Perspektiven?

Krisenresilienz: Wie werden OSM-Daten für Disaster Risk Reduction systematisch verbessert?

Für österreichische Mapper ist Paris ein Anlass, sich zu fragen: Welche österreichischen Best Practices sollten auf globaler Ebene geteilt werden? Sind es Micro-Mapping-Praktiken? Lokal-digitale Governance-Experimente? Accessibility-Ansätze?

Fazit: Kartographie als Verantwortung Diese Wochenkolumne hat versucht, einige Leitfäden durch das komplexe OSM-Ökosystem zu ziehen. Zusammengefasst:

Governance ist nicht optional. Eine transparente, inklusive Entscheidungsfindung – etwa durch die Sovereign Tech-Initiativen – ist zentral für OSM’s Zukunft.

Barrierefreiheit ist Datengerechtigkeit. Die Wheelmap zeigt, dass spezialisierte Accessibility-Mapping nicht Mainstream-Kartographie konkurrenziert, sondern ergänzt und verbessert.

Micro-Mapping ist Infrastruktur. Bänke, Lampen, Eingänge – diese Kleinigkeiten sind entscheidend für Inklusion.

Humanitäre Kartographie braucht lokale Expertise. Internationale Validation ohne lokale Community-Partizipation perpetuiert Machtasymmetrien.

Digitales Erbe zählt. OSHDB und ohsome API sind Archiv-Tools für zukünftige Generationen.

Wien ist nicht Peripherie. Die Leopoldstadt und das Nordbahnviertel sind Labor für europäische urbane Probleme – und OSM kann hier zeigen, dass freie Geodaten nicht nur für den Globalen Süden wertvoll sind.

Für die Woche 2 des Jahres 2026 bleibt die Aufgabe gleich: Kartographieren wir mit Verantwortung. Nicht für abstrakte „offene Daten”, sondern für die Menschen, die diese Daten nutzen werden – und für jene, die systematisch aus Kartographie ausgeschlossen wurden.

Quellen dieses Artikels:

Wheelmap News: OpenStreetMap als Basis der Wheelmap-Barrierefreiheitsdatenbank; OSM Community Forum: Governance von Core Software in OpenStreetMap; Accessible Maps Projekt: Indoor-Kartierung und Barrierefreiheit; Accessible Maps Kooperation mit Sozialhelden; Humanitarian OSM Stats: HOT Tasking Manager Validierungsprozesse; Population Density und Remote Humanitarian Mapping; Collaborative Disaster Mapping in Mozambique; Flood Impact Assessment in Lagos mit OSM; Deep Neural Network Gebäude-Erkennung Tanzania/Cameroon; Spatial-Temporal Analysis nach Naturkatastrophen; Micromapping-Definition und Tags; HOT Validator Training American Red Cross; HOT Validation Earthquake Response; Administrative Boundaries in OSM; OSHDB und digitales Erbe; Missing Maps und Deutsches Rotes Kreuz; Sovereign Tech Agency OSM-Investitionen; Vienna OSM Coverage Analyse; Wheelchair Tagging-Standards; POI-Qualitätsvergleiche; Pedestrian Data Trends 2024; State of the Map 2025 Trends; POI-Profiling Wohnimmobilien; OSM Austria Stammtische; Wheelmap Accessibility Coverage; Data Quality Approach LAC; OSM-AT Official Local Chapter; State of the Map 2026 Paris; Women-Inclusive Mapping HO

In the first 2026 edition of our OpenStreetMap interview series we speak with some of the makers of CoMaps, a community-driven, free and open-source, offline navigation app that uses OpenStreetMap data.

1. Who are you and what do you do? What got you into OpenStreetMap?

Will: I actually made my first edits, like many people, trying to make my area accurate for the Ingress and Pokemon Go. Later, I wanted an open source GPS app that didn’t track me, and found maps.me. I’m a web developer by trade, but learned C++ while trying to contribute to Organic Maps.

Bastian: By training, I’m a researcher coming from biology and bioinformatics, but I’ve spent the last 10 years or so working in different free & open knowledge spaces. I got into OpenStreetMap when I was visiting Vietnam a couple of years ago and Google Maps refused to deliver instructions for bicyle navigation. That’s how I tried Organic Maps and did my first OSM edits. From there, I went down the mapping rabbit-hole.

Anton: I was working in a big industry complex where finding the buildings was super hard, when I discovered I could add them in OSM for me and the rest of my colleagues. Immediately, I got hooked on the idea of fixing things for yourself and the human collective. Then, I went and mapped a lot in my country of origin for which Google Maps wasn’t making an effort, and we were making OpenStreetMap have a better coverage in the region. Later, I discovered MapsMe until I didn’t like the changes and left the app until a couple of years later when I discovered Organic Maps, and it was my beginning in the open-source universe.

Matheus: I am currently a Transportation Engineer. Like many people reading this interview, and as I said in a previous interview where OpenCage supported my community, I needed an offline map (Maps.me), which later I discovered I could improve myself. I tried improving Google Maps, Waze and Apple Maps too, but it was (and still is) hard to succeed making my edits online. With OSM, this was never a problem.

2. What is CoMaps? Why was CoMaps created last year? How is it going?

CoMaps is a free & open-source map/navigation app (mainly) for Android and iOS, that is based on OpenStreetMap data and focuses on a privacy-respecting, offline-map use. Users can download the map areas they need to their phone and then navigate those maps fully offline. In addition to OSM data, the app also includes offline Wikipedia-articles to points of interest that have those links saved in OSM.

CoMaps started in May 2025 as a fork of Organic Maps, which itself is a fork of maps.me, made by a group of volunteer contributors to Organic Maps. There are a couple of reasons for CoMaps forking from Organic Maps, centered around open source, transparency and shared decision making. These concerns were brought to the leadership of the company behind Organic Maps in an open letter. Given the lack of response and meaningful changes, CoMaps was been started to create a more open and community-governed project.

Since then, CoMaps has been progressing quite rapidly for a “new” project: In 2025 we made 12 releases and started implementing our own improvements. This includes updated map styles, an improved routing engine, increasing details shown for electric vehicle chargers, and the ability to use your own map server, to be less dependent on CoMaps infrastructure.

Outside the technology bits, we have also grown our community quite a lot. Both in terms of code contributors but also all the folks that volunteer their time for making translations, writing blog & social media posts, running language-specific community chats and many more.

3. Are the users of CoMaps typically already familiar with OpenStreetMap?

CoMaps is used by many different types of users, with different levels of familiarity with OpenStreetMap. Some of our most active users, and of course also contributors, are very familiar with OpenStreetMap and started using or contributing to CoMaps coming from the larger OSM world.

But there will be also many users who aren’t yet familiar with what OSM is and how to contribute to it. These are people who found CoMaps by way of a recommendation from their friends or family, or read about the project online when looking for map/navigation apps. As more and more people try to “degoogle” their lives, want to avoid “big tech” and lower their reliance on tools controlled by US corporations, the group of users who aren’t yet familiar with OSM but that just look for a replacement to commercial maps is likely to increase. Offline map apps are also really popular with backpackers and people with poor cell reception.

As time goes on, some users slowly “graduate” from being a pure consumer of OSM to becoming active contributors to OSM. This can start with using the basic OSM editor that’s part of CoMaps to make simple additions or leave notes, before moving to more dedicated and advanced editors. Bastian’s story of how he got into OpenStreetMap is a good example of that: His first contact to OSM was through using Organic Maps, which led him down the rabbit hole.

4. To what extent is the goal of the service “just” to provide a useful tool for navigation versus trying to bring users into the OpenStreetMap community and encouraging them to contribute?

We do see CoMaps as an important pathway for people to enter the OSM community with a low entry barrier, both through technical and social means.

On the technical level, we aim to keep the OSM editor inside CoMaps comparatively simple. It is focused on adding/editing simple Points of Interests and objects, without needing to know or understand the keys & tags of OSM. For more complex or potentially destructive edits (like removing objects), we allow users to submit notes to OSM instead, to avoid that well-meaning newcomers accidentally delete information. The submitting of notes instead of edits works even for users who don’t have an OSM account yet, as they will be submitted as anonymous notes. By keeping things simple, we aim to get people interested in contributing, to help them onboard to become more regular OSM contributors down the line. We also think a lot about how to communicate and “onboard” the concept that edits will be sent out to the world, and the map is made accurate through their contributions.

To help with this onboarding, we have the community of CoMaps: Contributing and editing OSM are frequent topics on our community channels, whether that’s in the Matrix chat rooms or on Mastodon, Reddit etc. People share their questions and advice on how to improve the data in OSM, and through this learn to contribute also outside of CoMaps.

We also try to facilitate this by sharing tips and suggestions on how to contribute to OSM on our project website, e.g. how to spot common tagging mistakes like typos that can help improve routing, or how to help by responding to notes left by other CoMaps users.

Through these different pathways of engaging with OSM through CoMaps and its community we try to grow the OSM community as well.

5. the sheer comprehensiveness of OSM can create a major challenge in terms of visualizing the data on a map. How do you solve this?

Ultimately, there is not a single way to “solve” this challenge, as different use cases require different maps and decisions for what to display on them and how. While this can be a challenge, it is also the big strength of OSM, as it enables creating maps tailored to those different use cases: Whether that’s bike navigation via BRouter, “vampire routing” to stay in the shade or plotting waterways.

For CoMaps we strive to create simple and readable maps that deliver a great experience for the most common “general purpose” map use cases, instead of diving deep into some or all of the potential specialist use cases. That means we prioritise creating maps that are very readable for general foot/bike/car navigation and exploration, displaying relevant POIs and objects, without cluttering the map with the full level of detail that OSM would theoretically afford.

The reasoning for this is that there is an inherent trade-off that generalist apps face. This is between simplicity or ease-of-use and how customizable/adaptable to specialist use cases your app can be. As you add more configuration or display options, your app becomes more and more able to support comparetively rare use-cases, but this comes at the cost at becoming much harder to understand due to this wealth of configuration options, which even affect the more common use cases.

Which isn’t to say that one approach is better than the other, as there are many specialist/niche use cases for maps for which other apps might be better suited than CoMaps is. Luckily, in the digital commons space around OSM & open source, we do not need to compete or be “the best app for everything”, instead we are happy if we can provide an app that works for most people and their most common map needs, and encourage people to use other free & open tools for the times where CoMaps isn’t ideal. We also happily work with those who do want a niche app, like BlueLight Maps which is a forked app for emergency vehicles and has contributed back to the project.

6. What is the best way for people to get involved in the CoMaps project?

There are many ways to get involved with CoMaps, as there’s always a lot of things to do. It’s important to note that writing code for the apps, while very important, is only one aspect of contributing. We can always need help with many non-code tasks too: Whether that’s writing documentation/help pages; making translations to other languages; creating graphics for the app or website; responding to support requests or inquiries on social media; or any other task, help is always welcome.

For people who want to get to know us better, the easiest entry point is probably our main Matrix room (which is also bridged to Telegram). But really any of our online chat groups can be a good entry point: www.comaps.app/community/

7. Last year OpenStreetMap celebrated 20 years. Where do you think the project will be in another 20 years?

It’s so hard to predict! Hopefully, much like the web and wikis, user-contributed data and apps will become even more common, and *“giving back”* will be more of an obvious choice. Google relies heavily on user submissions and edits with their maps, and many folks now have been raised on entertainment like Minecraft and Roblox where user-made content is not just key but even somewhat prestigious.

There’s also the aforementioned trend towards divesting from big tech, focusing more on privacy, and “degoogling” ones life. So the idea that you’re not being taken advantage of by a closed for-profit corporation is seeing some progress too! Which is all to say, hopefully both OSM and CoMaps will endure and even grow as public resources, unable to be walled off for private profit.

Thank you, CoMaps team! It is so important to move OpenStreetMap from niche, hobbyist community to tool used broadly (and thus also supported and maintained) by society. Services like CoMaps are such an important entry point for getting people into the community.

Keep up the good work, and I look forward to following your progress.

Ed

Please let us know if your community would like to be part of our interview series here on our blog. If you are or know of someone we should interview, please get in touch, we’re always looking to promote people doing interesting things with open geo data.

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:

• age= mature, semi-mature, young
• denotation=avenue, garden, park, street
• genus= Araucaria, …, Trachycarpus
• health= 0, … ,5
• height= 1, … , 25
• leaf_cycle=deciduous, evergreen
• leaf_type= broadleaved, needleleaved, palm
• natural=tree
• object:city=Pitrufquén
• object:street= Nombre calle
• species= Nombre científico
• species:es= Nombre vernáculo

El equipo de mapeo formado por tres personas: Paul Dassori (Awo): Coordinador y revisor principal del proyecto, con basta experiencia en el mapeo de árboles, y dos novatos capacitados para georeferenciar nodos de tipo árboles, postes eléctricos e hidrantes (grifos). Lograron determinar otras variables de los árboles como estimar alturas, identificación, tipos de hojas, ciclo de las hojas.

1. Amaru Dassori
2. Ariki Dassori
3. Paul Dassori

Resultados

• Se registraron 1780 árboles: se identificaron 1246 (70 %) de forma virtual, conformando 51 especies .
• Ciclo de la hoja (%): Perennes (62.1) 🍃, Deciduos (36) 🍂, Por deternimar (1.9) 🫢.
• Tipo de hoja (%) : Latifoliadas (83.7)🌳, Coníferas (10.3)🌲, Palmas (5.3)🌴.
• Estado Sanitario (%): muerto (0.05), gravemente enfermo (0.84), enfermo (9.6), levemente enfermo (29.3), sano con daños (36), sano (23).
• El 59% sanos😍 y 41% enfermos 🤒.
• Estado de maduración (%): maduros (19.4), semimaduros (30) y juvenil (46.7)
• Ubicación (denotation %): avenida (1.4), jardín (11.1), parque (9.8), calle (77.5).

Las especies más frecuentes (top 10) son:

• Maytenus boaria 341
• Nothofagus dombeyi 98
• Betula pendula 95
• Trachycarpus fortunei 81
• Quercus robur 55
• Acer pseudoplatanus 48
• Populus nigra 47
• Podocarpus salignus 45
• Acacia melanoxylon 37
• Pseudotsuga menziesii 48

Bonus track:

• Primera Municipalidad en Chile en registrar los árboles urbanos en forma sistemática usando las etiquetas estándar de OpenStreetMap, para natural=tree.
• Todos los árboles representados en 3D en la plataforma F4Map, caracterizados por la altura 📐y tipo de hoja🌲 🌳.
• Se mapearon adicionalmente 508 postes eléctricos, 166 tocones de árboles.

👇 Mapa de distribución de tocones (tree_stump) vistos desde MapComplete, en su nueva actualización (2026) en el tema de árboles.

Gracias a la Municipalidad de Pitrufquén, a encargado del Medio Ambiente Victor Flores Gonzalez, alias OSM VitoFlores, por la oportunidad de establecer y generar #DatosAbiertos para la constitución del #AUPV: Arbolado Urbano Público Virtual. 👉 🌲 🌳 🌴 📌 📸 🗺️ 🇨🇱

En hora buena, en los mapas de OSM cada vez serán más visibles los puntos verdes. Valdivia-Chile, 08 de enero 2026

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

The geographical and climatic landscape of Kenya is defined by extreme variability, a characteristic that has become increasingly pronounced in the context of global climate change. In the years leading up to 2025, Kenya experienced what has been described as a “climatic seesaw,” swinging from the worst drought in forty years to unprecedented deluges that submerged vast sections of the country. This volatility is not merely a localized weather phenomenon but a manifestation of broader regional shifts in the East African climate, influenced by the Indian Ocean Dipole and the El Niño Southern Oscillation (ENSO). Historically, Kenya has navigated recurrent cycles of droughts and floods, but the events of 2024–2025 reached a threshold that challenged both national infrastructure and community resilience. The March-April-May (MAM) long rains of 2024, intensified by El Niño patterns, resulted in flooding that affected 40 out of 47 counties. By June 2024, official reports from the National Disaster Operations Centre (NDOC) indicated that 293,200 individuals had been displaced, and approximately 250,000 learners were out of school due to the destruction of educational facilities and the use of schools as temporary shelters. The fatalities recorded during this period exceeded 290, with many individuals still missing as of late 2024. The economic impact of these floods is equally profound. The agricultural sector, which provides the livelihood for a significant portion of the Kenyan population, suffered immense losses. Over 65,000 acres of cropland were damaged, and 11,000 heads of livestock were lost, exacerbating food insecurity in regions that were already struggling to recover from the preceding drought. Critical infrastructure, including 68 roads and 45 health facilities, sustained heavy damage, creating logistical barriers to humanitarian aid delivery. This report addresses the need for a predictive and diagnostic tool that identifies the spatial distribution of these risks, allowing for more efficient resource allocation and targeted disaster preparedness.

The Role of Open Mapping and the HOT CWG Mentorship 2025

This research project is situated within the institutional framework of the Humanitarian OpenStreetMap Team (HOT) and its Community Working Group (CWG). The HOT CWG Mentorship Program was established to foster peer-to-peer learning and knowledge exchange within the humanitarian open mapping space. By pairing experienced geospatial professionals with emerging mappers, the program aims to build local capacity in priority countries, ensuring that those most affected by disasters are equipped with the tools to map their own vulnerabilities. As part of the “Audacious Project,” HOT has committed to mobilizing one million volunteers to map areas home to one billion people by 2025. This ambitious goal is predicated on the belief that maps and data, while not directly saving lives, provide the essential infrastructure for those who do. The transition from project-based work to a community-centered approach is vital for the sustainability of these efforts. This Legacy Project, authored by Yakubu Enoch and Alex Muruthi, represents a bridge between academic research and community-driven action. It utilizes open-source software and humanitarian datasets to create a reproducible model for flood risk assessment that can be adopted by OSM communities across Sub-Saharan Africa. The mentorship program emphasizes professional development, open geospatial skills, and data in humanitarian work. This project specifically addresses the “Open Geospatial Skills” and “Data in Humanitarian” focus areas by demonstrating how advanced GIS techniques, such as Weighted Overlay Analysis, can be applied to real-world crises. By publishing this work on the OpenStreetMap diary, the authors contribute to a global repository of knowledge, encouraging transparency, public reflection, and the continuous improvement of data quality within the OSM ecosystem.

Aim

To produce a Flood risk assessment map of Kenya.

Objective

a. Criteria Dataset gathering. b. Map creation and analysis using weighted overlay/raster calculation. c. Flood risk data interpretation.

Study Area Map of Kenya.

The study area encompasses the entire landmass of the Republic of Kenya, located in East Africa, spanning approximately 580,367 square kilometers. Kenya’s geography is marked by its diversity, ranging from the low-lying coastal plains along the Indian Ocean to the high-altitude Central Highlands, divided by the Great Rift Valley.

Literature Review

GIS and Flood Risk Assessment in Kenya

The application of Geographic Information Systems (GIS) and remote sensing in flood management has undergone a revolution in the past two decades. In the Global South, where ground-based meteorological stations are often sparse, these technologies provide a vital alternative for risk delineation and vulnerability analysis.

The Evolution of Flood Risk Modeling

Flood risk is traditionally conceptualized as the product of hazard, exposure, and vulnerability. Early models relied heavily on hydraulic and hydrological simulations that required extensive localized data. However, recent research has favored a multi-parametric approach using Multi-Criteria Decision Analysis (MCDA) and the Analytical Hierarchy Process (AHP). Studies in areas like the Eldoret Municipality have demonstrated that combining factors such as rainfall distribution, elevation, slope, and soil type can produce reliable risk maps with high validation accuracy. In the Western Region of Kenya, particularly the Budalangi sub-county, research has shown a strong correlation between altitude and flood risk. Analysis of Landsat satellite images reveals that 90% of flood risk zones are located below 1,144 meters above sea level. These zones are often covered by natural vegetation or farmlands, while “safe zones” are predominantly occupied by human settlements and administrative centers. This settlement pattern suggests a degree of historical adaptation, but the rapid expansion of populations into marginal lands is eroding these traditional safety margins.

Challenges of Data Quality and Uncertainty

A recurring theme in the literature is the challenge of data quality and uncertainty in flood prediction. The accuracy of flood maps is entirely dependent on the quality of input datasets, such as Digital Elevation Models (DEMs) and satellite rainfall estimates. Overlooking data uncertainty can lead to significant errors in estimating the intensity and timing of floods, resulting in misleading policy decisions. In Kenya, studies in the Lake Victoria Basin have utilized Satellite Rainfall Estimates (RFE) to overcome the lack of ground data, finding that while daily accumulations may vary, the products are effective for detecting rainfall occurrence and seasonal surges.

Methodology

Weighted overlay sum and raster calculation

The methodology for this national flood risk assessment follows a structured GIS workflow designed for reproducibility and technical rigor. The core of the analysis is the Multi-Criteria Decision Analysis (MCDA) framework, which allows for the integration of diverse environmental and social variables into a single risk index.

Methods

A. Criteria Selection and Data Acquisition

Six criteria were selected based on their established hydrological influence on flood generation and the availability of national-scale datasets:

i. Rainfall (30%): The primary hydrological trigger. Data was sourced from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), providing 35+ years of quasi-global rainfall time series.

ii. Distance from Water (25%): Proximity to the drainage network is a primary indicator of riverine flood exposure. Derived from OpenStreetMap waterbody and waterway layers.

iii. Elevation (20%): Determines the direction of flow and accumulation points. Data was obtained from the Shuttle Radar Topography Mission (SRTM) DEM.

iv. Slope (10%): Affects the velocity of runoff and the rate of infiltration. Calculated directly from the DEM.

v. Land Use/Land Cover (10%): Influences surface permeability. Data was sourced from the Copernicus Global Land Service 100m land cover maps.

vi. Distance from Road (5%): Acts as a proxy for infrastructure exposure and can influence local drainage patterns.

B. Dataset Pre-processing

To ensure compatibility, all datasets were projected to a consistent coordinate reference system and resampled to a uniform spatial resolution. Vector layers, such as roads and water bodies, were converted to raster format using GDAL vector-to-raster tools. Proximity analysis was conducted for roads and water bodies using the “Multi ring buffer” tool to create continuous distance surfaces.

C. Reclassification and Standardization

Because the input rasters have dissimilar units (e.g., millimeters for rainfall, meters for elevation, degrees for slope), they must be standardized into a common evaluation scale. A scale of 1 to 5 was adopted, where 5 represents the highest flood risk and 1 represents the lowest risk.

![Texte alternati] (https://commons.wikimedia.org/wiki/File:Reclassification_of_kenya_flood_risk_table.png)

For Land Use/Land Cover (LULC), specific weights were assigned to each class based on runoff potential:

1. Waterbodies: 5 (Source of hazard)
2. Bare land: 4 (Poor absorption)
3. Urban/Built-up: 3 (Low infiltration, high runoff)
4. Cropland: 2 (Moderate runoff)
5. Vegetation: 1 (Natural sponge, high absorption).

Weighted Overlay Calculation

The final flood risk map was generated using the Raster Calculator to apply a weighted sum overlay. Each reclassified raster was multiplied by its assigned influence weight and summed according to the following mathematical expression:

Use of Raster calculator in QGIS to get Flood Risk Index. Flood Risk Index = (Rainfall {reclass} x 0.30) + (Distance from water {reclass} x 0.25) + (Elevation {reclass} x 0.20) + (Slope {reclass} x 0.10) + (LULC {reclass} X 0.10) + (Distance from road {reclass} x 0.05)

The result is a continuous raster with values between 1 and 5, which was then categorized into five discrete risk classes: Very High, High, Moderate, Low, and Very Low.

Result

LULC

The LULC map, provided by the Copernicus Global Land Service, reveals the impact of human modification on flood dynamics. The expansion of built-up areas, particularly in Nairobi and Mombasa, has replaced natural pervious surfaces with asphalt and concrete, leading to rapid runoff during flash floods. Conversely, the preservation of forest cover in the Central Highlands remains a critical factor in mitigating the downstream impact of heavy rainfall.

Topography: Elevation & Slope

The DEM analysis shows that a large proportion of Kenya’s landmass consists of high-elevation plateaus. However, the low-lying coastal plains and the internal drainage basins (like the Tana River and Lake Victoria basins) are significant. The reclassified elevation map highlights these lowlands (below 500m) as “Very High” risk zones. Similarly, the slope map reveals that much of the Tana River basin and the coastal strip is remarkably flat (slope < 2°), which facilitates the ponding of water and slow-moving riverine floods.

Distance from Water and Road

The “Distance to Water” map creates a high-risk buffer along Kenya’s perennial rivers. This factor is critical for identifying areas susceptible to riverine flooding. The “Distance to Road” map highlights the intersection of human infrastructure and flood risk. Because major transport routes often follow valley bottoms or low-lying corridors, they represent significant exposure points for economic disruption.

Rainfall

The rainfall map, derived from CHIRPS data, highlights the stark contrast between the humid highlands and the arid north. During the 2024 El Niño cycle, coastal regions and the Lake Victoria basin saw anomalies significantly above the long-term average. The reclassification of this layer identifies these regions as the primary engines of flood risk, where the volume of water entering the system frequently exceeds the capacity of natural and artificial drainage.

The National Flood Risk Map of Kenya

The synthesis of these factors produces the final National Flood Risk Map of Kenya. This map provides a prioritized visualization of susceptibility, allowing for a nuanced understanding of where hazards are most likely to intersect with human activity.

Spatial Distribution of Risk Classes

1. Very High Risk: Concentrated in the Tana River Delta, the Lower Kano Plains near Kisumu, and the coastal strip including Mombasa. Urban informal settlements in Nairobi (Mathare, Kibera) also fall into this category due to high runoff and proximity to riparian zones.
2. High Risk: Includes the northern Rift Valley lakes (Turkana, Baringo) and the floodplains surrounding the Ewaso Ng’iro and Sabaki rivers. These areas face frequent inundation during strong “short rains” and El Niño seasons.
3. Moderate Risk: Covers transitional zones between the highlands and the lowlands, including the Arid and Semi-Arid Lands (ASALs) where flash flooding is a periodic threat.
4. Low to Very Low Risk: Located in the Central Highlands, the Aberdare Range, and the high-altitude regions of the Rift Valley. These areas have steep slopes and good drainage, making them safe from large-scale inundation, though they remain vulnerable to localized landslides.

Statistical Profile Of National Risk

The weighted overlay results indicate that while only a small percentage of Kenya’s total land area is at “Very High” risk, these zones contain a disproportionately high amount of the country’s population and critical infrastructure.

Recommendation

1. Ecosystem Restoration and Nature-Based Solutions The significant influence of Land Use/Land Cover on flood risk underscores the need for ecosystem restoration. The government should prioritize the restoration of degraded forests and wetlands in “water tower” regions to enhance natural water absorption. Implementing nature-based solutions, such as wetland restoration and the creation of riparian buffers, will improve the resilience of riverine communities.

2. Strategic Urban Planning and Infrastructure There is an urgent need for the implementation of spatial and development plans in urban areas. This includes improving garbage collection to prevent the blockage of drainage systems and relocating residents from the highest-risk riparian zones to safer, planned housing. Infrastructure like roads and bridges must be designed to withstand projected increases in rainfall intensity, particularly in the “Very High” risk coastal and lake regions.

3. Empowering the Open Mapping Community As a HOT CWG Legacy Project, this study recommends the continued empowerment of local OSM chapters. The mapping of “Very High” risk areas should be prioritized in the HOT Tasking Manager, with a focus on capturing high-resolution building footprints and drainage networks. Community-led data integration and participatory mapping will ensure that risk maps are grounded in the lived reality of vulnerable populations.

4. Early Warning Systems and Anticipatory Action Kenya needs to adopt more effective early warning systems that provide timely and accurate information to residents in high-risk zones. By integrating real-time satellite rainfall estimates (like CHIRPS) with the risk map produced in this study, disaster managers can trigger “anticipatory action” protocols—providing cash transfers or evacuation assistance before the floodwaters arrive.

Conclusion

The 2025 National Flood Risk Assessment of Kenya provides a rigorous spatial framework for understanding the nation’s vulnerability to hydrometeorological extremes. By synthesizing environmental and anthropogenic factors using GIS and Multi-Criteria Decision Analysis, the research has identified the specific geographical clusters where risk is most acute. The transition from the 2022–2023 drought to the 2024–2025 floods serves as a stark reminder that the traditional patterns of climate in Kenya are shifting, requiring more sophisticated and data-driven approaches to disaster management. The Legacy Project for the HOT CWG Mentorship 2025 fulfills its aim of producing a functional flood risk map and providing a technical pathway for future researchers and humanitarian mappers. The findings reveal that while the highlands remain relatively safe, the low-lying basins and urban informal settlements face a “Very High” risk of catastrophic flooding. Addressing these risks requires a multi-faceted approach: restoring ecosystems, climate-proofing infrastructure, and, most importantly, empowering local communities with open geospatial data. This map is not a final product but a living tool for the OpenStreetMap community and its partners as they work toward a more resilient and prepared Kenya. Through the continued exchange of geospatial skills and the mobilization of global volunteers, the vision of the “Audacious Project” can be translated into tangible safety for those living on the front lines of the climate crisis.

Work Cited

1. Kenya Crisis Response Plan 2024, https://crisisresponse.iom.int/response/kenya-crisis-response-plan-2024
2. Life after Kenya’s floods of 2024 - PreventionWeb, https://www.preventionweb.net/news/life-after-kenyas-floods-2024
3. Flood Havoc in Kenya Underscores Climate Adaptation Need, https://climateadaptationplatform.com/flood-havoc-in-kenya-underscores-climate-adaptation-need/
4. Kenya Floods Recovery Needs Assessment - United Nations Development Programme, https://www.undp.org/sites/g/files/zskgke326/files/2025-05/kenya_floods_recovery_needs_assessment_2024.pdf
5. Humanitarian OSM Team/Working groups/Community/Mentorship - OpenStreetMap Wiki, osm.wiki/Humanitarian_OSM_Team/Working_groups/Community/Mentorship
6. Assessment of flood risk using space technology in Matuga state …, https://www.space4water.org/news/assessment-flood-risk-using-space-technology-matuga-state-kenyas-coastal-area

7. Creating a Flood Map Simulation with QGIS

   by K Yashima

   Helios-techblog - Medium, https://medium.com/helios-techblog/creating-a-flood-map-simulation-with-qgis-8a9beed0a2b8. Flood conditioning factors: (a) Distance to roads, (b) Distance to streams, (c) Rainfall, (d) Lithology. - ResearchGate, https://www.researchgate.net/figure/Flood-conditioning-factors-a-Distance-to-roads-b-Distance-to-streams-c-Rainfall_fig4_378610335

8. Multi Criteria Overlay Analysis (QGIS3) — QGIS Tutorials and Tips, https://www.qgistutorials.com/en/docs/3/multi_criteria_overlay.html
9. The Risk of Flooding to Architecture and Infrastructure amidst a Changing Climate in Lake Baringo, Kenya - Scirp.org., https://www.scirp.org/journal/paperinformation?paperid=123556
10. The Risk of Flooding to Architecture and Infrastructure amidst a Changing Climate in Lake Baringo, Kenya - Scirp.org., https://www.scirp.org/journal/paperinformation?paperid=123556
11. Mapping Land Use Land Cover within Flood Risks and Safe Zones …, https://www.journals.eanso.org/index.php/eajenr/article/view/2943

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:

The estimate I used for this graph is quite simply that the number of buildings is half of the number of residents of the county. This probably isn’t a great estimate, and I’d love if someone knows how to pull better estimates from somewhere, but it’ll do for now. Based on this estimate, we’ve only kept pace with new construction since 2022 or so. There’s lots of work left to do!

A few other highlights from the past couple months:

• I mapped all buildings and most landuse in Marlboro Township, the county’s smallest township by population.

• Relatedly, I added a good chunk of forest landuse around the Delaware and Alum Creek reservoirs, with more left to do.

• I significantly improved the fairgrounds, with plans to do more once I have the chance to make a trip there in decent weather. Hopefully, OSM-based apps will be a competent navigation aid during the next fair.

• I recently started tackling the over 600 unnamed highway=residential ways in the county. Some of these are miscategorized, most of them are new construction that needs someone to visit it in person. I’m trying to remember to take some detours whenever I drive by one of those areas. Some of these might also have available streetside imagery, I haven’t checked.

​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.

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.

This time, the system returned a different graph traversal. From Cawang Sentral, take route 9 (Pinang Ranti–Pluit) toward Pluit, get off at Pancoran Barat, then transfer to route 5N (Kampung Melayu–Ragunan).

That worked.

At this point, I had two viable route “recipes.” I sent the information and considered the problem solved.

Then, around 1 p.m., a new data point arrived.

She was at Patra Kuningan stop.

Patra Kuningan?

That node was not part of any plan.

I immediately re-opened the Transjakarta app and searched for Patra Kuningan. Turns out, there was a direct route from there: route 6, Ragunan–Galunggung.

So yes, it was still reachable.

But this raised a more interesting question. Had all the careful route planning – that i made before– been completely unused?

I wanted to verify this, not anecdotally, but spatially.

What I needed was an application that could display the coordinates of all these stops at once, so the entire system could be visualized as a rough operational map rather than isolated routes.

I couldn’t find (the right) one.

Well, I actually found some. For example, uMap. But I need something much simpler than uMap. Here’s why.

“There are too many clicks and steps if you want to use uMap.”

You use a search engine, search for uMap, arrive at https://umap.openstreetmap.fr/en/ , click “create a map”, click the marker icon, add some description, then click “close”. Oh, I need the label to be shown by default. But in this uMap instance, the label is hidden by default. So I need to show it. How do I show the label? Right-click the marker, then… umm… think for a few seconds. Ah. “interaction options”! So, click interaction options, click display label, click “always” instead of “never”. Great, now let’s add more place markers. But no. That “always” option is only enabled for that specific place marker. So when you add a new place marker, it’s back to the “hidden label” preference. Oh no. Should I do this one by one? Hmm.

After thinking and tinkering for a few minutes, I finally solved the puzzle. It’s the layer. We should modify the interaction options, specifically display label, at the layer level instead of individual place markers.

But… ah, I give up.

I decided to make my own app. A simpler app.

So I built it: Altilunium Locationpad. Done.

Let’s proceed to my original mission. Here we go :

Once the map was plotted, the situation became clearer.

If they were at Patra Kuningan, the train-based plan was definitively abandoned. There is no realistic path that involves a train and ends up at Patra Kuningan. The expected region after a train transfer would have been Pasar Minggu or its surroundings, and on the map, Patra Kuningan sits far north of that cluster.

The only plausible explanation was this: they chose the busway-only option. Vida to Cawang Sentral, then route 9 toward Pluit. The intended transfer point should have been Pancoran Barat, but they likely stayed on the bus too long and ended up at Patra Kuningan instead.

From a spatial perspective, that overshoot wasn’t catastrophic. Pancoran Barat and Patra Kuningan are not that far apart. The error margin was tolerable.

My original suggestion was to transfer at Pancoran Barat and take route 5N straight to Ragunan.

In reality, from Patra Kuningan they took route 6, Galunggung–Ragunan.

Different edges, same destination.

There’s also another plausible execution path. They may have ignored my plans entirely, taken Vida to Cawang Sentral, and asked the on-site staff, “From here, how do we get to Ragunan Zoo?” The answer would have been operationally valid, though slightly suboptimal: take route 9 toward Pluit, get off at Patra Kuningan, then transfer to Galunggung–Ragunan.

From a rough efficiency calculation, that advice sends passengers a bit too far north. Pancoran Barat is geographically closer to Ragunan and would have minimized backtracking.

But in the end, the system converged.

They arrived in Ragunan.

At 3:50 p.m., the objective was achieved, and the meeting with Masbro finally happened.

When I first started the ESA Validation Fellowship, I’ll admit I felt like a bit of an outsider. Even though I loved mapping, I often struggled with imposter syndrome, wondering if my skills actually measured up to the “expert” level. Looking back now, the growth I’ve experienced is incredible. I remember how intimidating JOSM used to feel with all its complex buttons and menus, but through this fellowship, it has become like a second language to me. I’ve moved past the basics and now feel completely at home using advanced tools to clean up and verify data. I learned new shortcuts and got to publicly map as others watched, proving to myself that I belong in this space.

Working alongside such talented people helped me realize that I really am an advanced mapper and a capable validator. Seeing my work hold up next to theirs finally silenced that voice in my head telling me I wasn’t good enough.

But as much as I’ve grown technically, the heart of this experience has been the people. Connecting with a global community of mappers who share the same passion for “mapping for change” has been the most rewarding part of the journey. The fellows were always helping each other out and in constant communication, a true community and something that happens only when people have common goals beyond borders. Together, we’ve been able to support humanitarian efforts and help communities become more resilient by making sure the maps they rely on are accurate and high-quality. Whether it’s a rural village or a growing city, I now understand that a precise map can be a lifeline.

This fellowship has changed how I see my work and myself. I’m walking away with new skills and if I could coin a phrase for the ESA Validation Fellowship, it would be : Certainly, what we do truly matters for the world.

During the ESA HOTOSM Validation Fellowship, my journey began with the basics—learning how to install JOSM on my laptop and understanding its interface. From there, I learned how to install and use essential plugins such as the Building Tools plugin, Utils plugin, Mapathoner, and other supporting tools that greatly improved my workflow and actually made my validation easy. I also learned how to install and apply map paint styles, which helped me easily detect issues such as overlapping buildings and missing or incorrect tags, learnt how to work with different imagery such as bing, Esri and how to deal with the imagery offsets. These foundational steps laid the groundwork for my growth as a validator and deepened my understanding of data quality in OpenStreetMap. As the fellowship progressed, we moved into active validation, where I gradually became familiar with new JOSM shortcuts that made mapping and validation more efficient. Shortcuts such as B for drawing buildings, G for ungluing objects, and Ctrl + Shift + G for replacing the geometry significantly improved my speed and accuracy. As someone who had not done validation before, the daily practice sessions were extremely valuable. Validating tasks across different regions allowed me to learn by doing, and with each task, my confidence and skill level improved. This consistent hands-on practice reinforced the idea that practice truly makes perfect.

One of the most impactful aspects of the fellowship was the opportunity to receive personal guidance. I had several private sessions with some of the team members, where I received direct feedback, clarification on complex validation issues, and encouragement to keep improving. These sessions helped me better understand quality standards, common mapping mistakes, and how to communicate effectively with mappers. Through this mentorship and continuous practice, I grew from a beginner in validation to someone who is now confident in reviewing and improving mapping data.

Throughout the fellowship, I also validated data across different regions, including Africa and other parts of the world. This exposed me to variations in settlement patterns, building shapes, and infrastructure layouts. By using filters, imagery tools, and map paint styles, I was able to identify inconsistencies such as overlapping polygons, misaligned features, and missing attributes. This experience gave me a broader perspective on how global mapping standards are applied in diverse geographical contexts and strengthened my attention to detail as a validator.

Balancing the fellowship with my academic responsibilities was sometimes challenging, as the training period overlapped with my lectures and examinations. However, the availability of recorded sessions ensured that I could catch up whenever I missed a live session. This flexibility allowed me to remain consistent and committed throughout the program. To further enhance the fellowship experience, I would suggest more flexible scheduling and additional interactive sessions to encourage stronger connections among fellows.

My name is Justus Aikiriza, a passionate mapper and Land Surveying and Geomatics Engineering student from Uganda. I joined HOTOSM with the desire to make a meaningful humanitarian impact through mapping. Through this fellowship, I have not only gained strong technical validation skills but also built confidence in my ability to contribute high-quality OpenStreetMap data. Today, I am confident in validation, committed to quality over quantity, and motivated to continue supporting humanitarian mapping initiatives across the ESA region and beyond.

Also adding to my introduction, my first impression was the time I was selected among the people to participate in the fellowship, I got so happy coz I had always waited and wanted this opportunity, so me being chosen among didn’t really take that chance for granted and really appreciate God for this together with Madam Becky Chandu our organizer and the entire HOT fraternity for this wonderful arrangement. By the time I was selected, we were actually in exams buh I had create some time each time to make sure I contribute, ask questions where I didn’t understand coz to me it was an opportunity, and now I have all it takes to actually volunteer as the Validator and mapper. Thank you so much. May God Bless you 🙏🙏

My Growth Journey in the ESA validation Fellowship

When I was first selected for this fellowship, I felt a mix of excitement and nerves. My first major assignment was Hot Tasking Manager Project #16505, and I’ll be honest: it was a wake-up call. Initially, the tasks felt daunting. I quickly realized that while I knew how to map, I hadn’t yet mastered the advanced features required to validate data efficiently.

My workflow was slow, and I felt I was missing the technical “bridge” needed to ensure the high-quality data that a project of this scale demands. The breakthrough came through the mentorship of our facilitators. They introduced us to a suite of professional techniques and GIS tools that changed everything. Specifically, learning how to leverage advanced filters and JOSM shortcuts was a game-changer.

These weren’t just “tips” they were the keys to unlocking a much more efficient and precise validation process. By integrating these tools into my daily workflow, my technical hurdles disappeared. What once felt overwhelming now feels intuitive. This fellowship has done more than just teach me how to click buttons; it has provided me with:

1. Technical Proficiency: A deep understanding of GIS features I previously overlooked.
2. Confidence: The ability to validate complex data with precision.
3. Professional Growth: A clearer perspective on the standards required in the GIS profession.

I am walking away from this experience with full confidence in my skills and a renewed passion for contributing high-quality data to the OpenStreetMap community

Introduction

The ESA Hub Fellowship was a deeply enriching and practical learning experience that significantly strengthened my skills in geospatial data production, validation, and humanitarian mapping. From the beginning, the fellowship introduced me to the mission of ESA Hub and the critical role that open geospatial data plays in disaster response, resilience building, and inclusive decision-making. I developed a strong understanding of the OpenStreetMap (OSM) ecosystem, humanitarian mapping principles, and the workflows of the HOT Tasking Manager, which laid a solid foundation for meaningful contributions to real-world projects.

Throughout the fellowship, I gained extensive hands-on experience in mapping and contributing to several humanitarian and disaster response projects. Using tools such as iD Editor and JOSM, I digitized key features including buildings, roads, waterways, and other critical infrastructure from high-resolution satellite imagery. I actively contributed to projects supporting humanitarian response in Sudan, Mapping for disaster resilience in Elgeyo Marakwet, and emergency response efforts for Hurricane Melissa in Jamaica, among many other projects. These contributions helped improve the availability and quality of geospatial data in under-mapped and disaster-affected regions, supporting responders and planners on the ground.

A major highlight of the fellowship was my involvement in data validation and quality assurance. I participated in multiple levels of validation, including in-depth third-pass validation of OSM data. This process required careful inspection of geometry accuracy, correct tagging, completeness, and adherence to OSM and HOT standards. Through this experience, I developed a strong eye for identifying common mapping errors such as misaligned features, incorrect classifications, duplicated objects, and incomplete networks. I also learned how to provide clear, constructive feedback to mappers, contributing to continuous learning and improved data quality within the community.

The fellowship further exposed me to advanced tools and workflows used in humanitarian mapping and data quality monitoring. I worked with JOSM validation tools and plugins, cross-checked multiple imagery sources to improve accuracy, and reviewed changesets to ensure consistency and reliability of mapped data. Beyond technical skills, the program emphasized collaboration and community engagement. Interacting with mappers from diverse backgrounds helped me appreciate the power of collective effort in building open, reliable geospatial datasets for global impact.

Overall, the ESA Hub Fellowship was a transformative experience that enhanced my technical expertise, analytical skills, and understanding of community-driven mapping for disaster response. It reinforced the importance of high-quality, validated geospatial data in humanitarian contexts and strengthened my commitment to using geospatial technologies for social good, disaster resilience, and sustainable development.

As of December 13th, 2025, Swift and Portal interlockings have been renamed to “Old Swift” and “Old Portal”. Tracks and signals have also been renamed (2 turned into 22 and 3 turned into 33). I’ve already put in the edits.

OSM: osm.org/#map=16/40.75308/-74.09527

Openrailwaymap: https://openrailwaymap.org/?style=standard&lat=40.7532&lon=-74.1037&zoom=15

NYW1-23-b: https://archive.org/details/AMTK-NEC-employee-timetable-supplemental-bulletin-20251213-NYW1-23-b

Current NEC ETT: Amtrak - Northeast Corridor Employee Timetable 2025-11-03, Special Instructions

As always, I put all bulletins and new employee timetables in this list on Archive.org

Amtrak’s FOIA office is now really fast. I can get bulletins the same day they are requested. Back in August they were much slower but now that I’m doing these every month they are on top of it.

Revisei recentemente áreas da Serra fortemente afetadas pelas chuvas da enchente de 2024. Um trecho da BR-470 na região da Ponte dos Arcos sofreu múltiplos deslizamentos e, 19 meses depois, ainda opera em sistema de comboio (sentido único reversível com escolta), com longas esperas, tornando várias estradas vizinhas rotas alternativas importantes.

Ao refazer o levantamento ali, notei uma quantidade significativa de nova sinalização de advertência, principalmente para risco de desmoronamentos. Como o OsmAnd agora oferece suporte básico à etiqueta hazard, passei a mapear esses riscos quando há placas de advertência no local, pois tendem a permanecer relevantes por muito tempo.

Pensando na utilidade prática para navegação, especialmente à noite e sob chuva, decidi focar o mapeamento de hazard apenas em dois riscos: desmoronamentos ( hazard=landslide, que podem influenciar a escolha da rota quando há chuva intensa) e animais ( hazard=animal_crossing, uma fonte comum de acidentes). Outros riscos sinalizados são frequentes, redundantes ou inferíveis pela geometria da via e tendem mais a poluir avisos de navegação do que a ajudar.

Notei que a RSC-287 ainda tem 5 pequenos desvios não totalmente recuperados, quase todos mal sinalizados e com acidentes fatais recentes, mas não vi novas placas para risco de alagamento ( hazard=flooding ). Por isso, sigo usando apenas flood_prone=yes em vias com histórico recorrente de alagamento após chuva intensa, com base em notícias (infelizmente raramente precisas) e na análise de imagens históricas do Sentinel-2, adotando o critério sugerido no wiki de a via permanecer submersa por mais de 0,1% do tempo (8h por ano, ou 1 dia a cada 3 anos).

During the fellowship, I learned how to validate more effectively, especially by using filters, search functions, and setting up map paint styles. I became better at identifying issues, mapping across different countries, and validating data from other regions. This helped me understand mapping more deeply, including the different shapes of buildings across countries. I also gained a stronger grasp of quality standards and grew more comfortable using JOSM shortcuts. For example, while validating building footprints in Africa and later in Asia like Japan, North and South America, I noticed differences in building construction styles and settlement patterns. By applying filters and map paint styles, I was able to quickly identify inconsistencies such as overlapping polygons or missing tags and correct them. This experience not only improved my technical validation skills but also gave me a broader perspective on how mapping standards can be applied across diverse contexts.

During my validation mapping in Japan, I encountered a task where the same area had been mapped using two different imagery sources of Bing and Esri. This created alignment issues and inconsistencies in the data. Through the fellowship, I learned how to handle imagery offsets, switch between imagery layers and use search tools to trace a particular mapper’s edits. By applying these skills, I was able to identify the discrepancies, adjust the imagery and improve the overall quality of the map. This experience was particularly meaningful because it showed me how technical validation techniques like managing imagery sources and targeting specific edits can directly enhance data accuracy. It was a proud moment to see how my improved skills contributed to cleaner, more reliable mapping outputs.

One of the challenges I faced during the fellowship was that the training period overlapped with my lectures and exams. At times, I struggled to balance both commitments. However, I was grateful that the recordings were always available as a backup, which allowed me to catch up and stay on track. If I could improve the fellowship, I would suggest adding more flexible scheduling and interactive sessions to help balance learning with other commitments and make more friends.

I am Justine Cyurinyana from Rwanda, proud to be a passionate mapper and humanitarian. My inspiration to join HOTOSM was to make a greater impact, and through this fellowship I have improved my skills, gained confidence in mapping and validation and had the opportunity to train and learn from others. With over 160,000 buildings, 5,000 km of highways and 2,000 km of waterways mapped, I focus on quality over quantity and hope to continue creating meaningful impact through mapping. I am grateful to ESA Hub for the opportunity and especially to Rebecca Chandiru for being an amazing mentor. She was always there to inspire us even in the midst of confusion and hundreds of questions, she responded swiftly and guided us in the right direction. To my fellow fellowship team, you all made this journey possible. I learned from each of you, even in private conversations when I asked questions and received quick answers.

I am proud of the validator I am becoming proud, bold and confident in contributing quality data. I am here to make more impact.

No dia 3 de janeiro de 2026 o grupo informal OSM Braga organizou um pequeno encontro e mapathon. Em 2024 chegámos a fazer vários encontros (físicos e online) entre maio e agosto, mas em 2025 estivemos apenas em contacto virtual através do nosso grupo no Telegram, e ao aproximar-se o final do ano, o o_andras lançou o desafio de nos voltarmos a encontrar, iniciando e coordenando a conversa que veio a resultar neste evento. Já não deu para ser em 2025, mas em contrapartida inaugurámos 2026 em grande estilo — batemos o nosso recorde de participantes, passando de 5 para 9 (poucos, mas bons! 😁)

Além de mapeadores da cidade e arredores, juntaram-se também membros da comunidade vindos do Porto e até Lisboa (graças ao apoio da Wikimédia Portugal que se ofereceu para cobrir despesas de deslocação dos participantes). Também tivemos uma diversidade interessante de níveis de experiência, desde mapeadores experientes até curiosos que nunca tinham editado o OSM.

Vários de nós já nos conhecíamos de encontros anteriores, mas dada a presença de algumas caras novas (🎉), começámos por nos apresentar e falar um pouco da nossa experiência com o OSM, e pelo meio conversar um pouco sobre vários temas, desde mobilidade pedonal e ciclável, mapeamento de transportes públicos, coordenação com entidades públicas e privadas, etc.

Depois, arregaçámos as mangas e fizemos algum mapeamento nos portáteis, onde a partilha de know-how foi bastante frutífera, quer entre editores experientes e novatos, quer entre os que já conhecem mais a fundo o ecossistema OSM, e que, graças aos interesses diferentes, e à imensidão de possíveis temas de especialização, puderam todos aprender alguma coisa nova.

No final, saímos à rua para mapear com StreetComplete, EveryDoor e Go Map!!. Também aproveitámos a oportunidade para tirar fotografias para o Wikimedia Commons e capturar imagens de nível de rua para o Mapillary.

Antes de irmos embora, ainda tirámos uma foto de grupo, e fizemos algumas mini-entrevistas em vídeo para registo da experiência e recolha de opiniões dos participantes, como forma de ajudar a divulgação de próximos eventos, e recolher feedback sobre como podemos melhorar eventos futuros. Um ponto muito positivo que emergiu dessas conversas finais foi a vontade expressa por vários participantes de que o evento tivesse durado mais tempo, para podermos aprofundar mais quer a troca de impressões e conhecimento, quer o mapeamento em si.

Estou certo que haveremos de nos encontrar mais vezes e continuar a reforçar esta pequena comunidade que tem aos poucos crescido desde que nos encontrámos pela primeira vez no Open Knowledge Braga 2024. Se leste isto e ficaste interessado em participar em futuros encontros, junta-te ao nosso grupo no Telegram!

🖼️ Categoria no Wikimedia Commons com fotografias do evento

ESQUEMAS DE ETIQUETAS PARA MAPEAMENTO TEMÁTICO COM OPENSTREETMAP PUBLICADOS EM 2025

[English below]  

A empresa IVIDES DATA, associada ao Instituto Virtual para o Desenvolvimento Sustentável - IVIDES.org, publicou em 2025 quatro esquemas de etiquetas para mapeamento temático com OpenStreetMap - vilas rurais, acesso a praias, toponímia e mineração. Os documentos são resultantes de novas pesquisas conjuntas e sessões de treinamento realizados com três instituições de ensino superior brasileiras: Universidade do Vale do São Francisco - Univasf, Universidade Federal de Minas Gerais - UFMG e Universidade Federal do Rio de Janeiro - UFRJ.

Espera-se que o material gerado seja adotado em mapeamentos colaborativos que não são tão frequentes, mas que possuem importância estratégica para o Brasil e os demais países. Um dos guias está em inglês e fará parte de publicação internacional sobre acesso a praias (uma iniciativa franco-brasileira), a ser lançada ainda em 2026.

[PT] Gabarito geral para o mapeamento de vilas rurais com OpenStreetMap

[EN] Collaborative mapping of beach access with OpenStreetMap

[PT] Esquema de etiquetas no OpenStreetMap - Toponímia

[PT] Esquema de etiquetas no OpenStreetMap - Mineração

A empresa tem registro formal no Brasil (CNPJ 56.127.866/0001-12) e está aberta a novas parcerias. Contatos podem ser realizados pelo e-mail ivides [at] ivides.org.

TAGGING SCHEMES FOR THEMATIC MAPPING WITH OPENSTREETMAP PUBLISHED IN 2025

The company IVIDES DATA, associated with the Virtual Institute for Sustainable Development - IVIDES.org, published four tagging schemes for thematic mapping with OpenStreetMap - rural villages, beach access, toponymy, and mining. The documents are the result of new joint research and training sessions conducted with three Brazilian higher education institutions: the University of the São Francisco Valley - Univasf, the Federal University of Minas Gerais - UFMG, and the Federal University of Rio de Janeiro (UFRJ).

We hope that the guidelines can be adopted in collaborative mapping projects on topics that are not so common, but which are of strategic importance for Brazil and other countries. One of the guides is in English, as it will be part of an international publication about beach access (a French-Brazilian initiative) to be launched in 2026.

[PT] Gabarito geral para o mapeamento de vilas rurais com OpenStreetMap

(General tagging scheme for mapping rural villages)  

[EN] Collaborative mapping of beach access with OpenStreetMap

[PT] Esquema de etiquetas no OpenStreetMap - Toponímia

(OpenStreetMap tagging scheme for toponymy)  

[PT] Esquema de etiquetas no OpenStreetMap - Mineração

(OpenStreetMap tagging scheme for mining)  

The company is formally registered in Brazil (CNPJ 56.127.866/0001-12) and is open to new partnerships. Contacts can be made by email at ivides [at] ivides.org.

This text was translated with DeepL.com and revised by a human.

Our White label sites system allows organisations and companies to embed an active travel route planner into their websites easily.

You can see some examples of the system in use at these sites:

• Ways Around The Bay (Morecambe Bay) – Plan your route
• Suffolk On Board – Dare to explore
• Cumbria Travel Actively – journey planner
• Cycle Ashford – route planner

Today we launch the latest release. We’ve added/enhanced a range of useful features:

• A fresh new mobile-friendly interface
• 3D itinerary mode
• ‘How far can I travel from here?’ – travel isochrones
• E-bike routing
• Elevation display with scrubbable control
• Browsing curated routes as both map/card
• New POIs display

These enhance the main A-B routing, optional circular routing, multiple waypoints, choice of map display, curated routes, and various navigation features.

.

A new, user-friendly mobile display with 3D mode.

New isochrones feature – shows how far from a starting location you can travel.

New 3D itinerary mode, with elevation display/scrubber.

Improved curated routes system with various filters and map-based display option.

Details of the White label sites system can be found on the Gov.uk Digital Marketplace, where we are an approved supplier.

Do get in touch if you would be interested in an installation for your site.

It is very easy to get running in technical terms: we use the same method as embedding a YouTube video, and we provide a handy control panel to set up the options and styles you want.

The full feature list is:

• Attractive, fully-zoomable map
• Choice of background layers – outdoors / cycle map showing NCN and local routes / satellite
• Plan routes from A-B
• Route choices: quiet, balanced, fastest, e-bike, walking
• Draggable markers, multiple waypoints
• Turn-by-turn directions with clickable points
• CO2 saved and calories count included
• E-bike routing, not available on most other websites
• Walk routing built-in (optional extra)
• Isochrone distance
• Elevation display/scrubber
• Plan circular A-A routes by time/distance (optional extra)
• Points of interest, with a wide range of categories you can manage directly
• Browsable preset leisure routes with photos and descriptions
• Data from OpenStreetMap, on which we can provide optional training
• Mobile-friendly with 3D mode
• Quick links to frequent locations
• Print output
• GPX output for main route types
• Share buttons
• Permalinks to enable routes and other pages to be shared easily
• Data refreshed daily