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.

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

Für das Mappen komplexer räumlicher Strukturen sind 360° Street View Bilder eine großartige Unterstützung. Schnelligkeit und Vollständigkeit der Datenerfassung sind die größten Pluspunkte. Einzig die hohen Kosten waren für mich abschreckend.

Das ändert sich derzeit. Die erste und zweite Generation der Consumer 360°-Kameras kommt nun zu sehr günstigen Preisen in den Gebrauchtwarenhandel. Die Samsung Gear 360 Modell 2017 ist z.B. ab 20€ zu haben. Da musste ich zugreifen :-)

Kamerahalterung fürs Fahrrad

Ziel war, so schnell wie möglich Testfahrten machen zu können. Es kamen daher nur Komponenten in Frage, die im eigenen Lager oder beim Händler um die Ecke vorrätig waren. Das Design spielte keine Rolle, es sollte nur beim Fahren nicht stören und bei Nichtgebrauch rasch demontierbar sein.

Die Konstruktion zeigen die folgenden Bilder:

Bild 01: DIY Stockstativ fürs Fahrrad (Zoom). Länge = 165cm

Bild 02: Montage am Fahrrad mit Rohrschellen (zoom). Rohrschellen mit Gummieinlage und M8 Anschlussmutter. Das Objektiv ist auf etwa 2,10 Meter Höhe

Das Stockstativ wird mit isolierten Rohrschellen am Fahrrad befestigt. Die Rohrschellen sind in jedem Baumarkt in diversen Durchmessern zu haben. Mit der oberen Befestigung wird auf das Lot ausgerichtet: verschieben entlang des Gepäcksträgers verändert die Neigung in Fahrrichtung; die Einschraubtiefe der äußeren Rohrschelle verändert die Neigung quer zur Fahrrichtung.

Prinzipieller Datenfluss

1. Fotoaufnahmen (beide 180° Objektive gleichzeitig) in periodischen Intervallen
2. Fotoaufnahmen mit Zeitstempel versehen
3. GPS aufzeichnen (Ort und Zeit)
4. Aus zwei 180°-Bildern eine Kugelpanorama erstellen
5. Geotagging des Panoramas

Es gibt mehrere Möglichkeiten diesen Datenfluss automatisiert ablaufen zu lassen. Sie unterscheiden sich deutlich in Aufwand und Qualität.

Richtwerte für periodische Foto Intervalle

Zur Orientierung einige Richtwerte, die für mein Gefühl einen guten Kompromiss zwischen Redundanz und flüssigen Bewegungsablauf auf Mapillary eingehen:

Die Redundanz ist wegen der signifikanten Rechenzeit für die Erstellung des Kugelpanoramas von Bedeutung. Der Speicherbedarf ist das kleinere Problem.

Realisierung #1: mit Samsung App Gear 360

Die Samsung App wird nicht mehr gepflegt und läuft daher nur auf älteren Smartphones. Kompatible Betriebssysteme sind iOS 10.0 bis iOS 13.0 und Android 6 bis Android 10.

Die Samsung App managt den gesamten Datenfluss von der Aufnahme bis zum georeferenzierten Kugelpanorama selbständig, eine Anwenderintervention ist nicht erforderlich.

Zumindest fast: Der Samsung App fehlt die Funktion Intervallaufnahme. Für den gedachten Zweck, müsste man der Auslöser periodisch selbst betätigen. Das ist klarerweise nicht praktikabel.

Mit einer weiteren App - Auto Clicker - lässt sich aber auch das automatisieren. Die minimale Intervallzeit liegt bei etwa 3s (Android 8 Smartphone Galaxy S7 edge). Limitierend ist vermutlich die Bluetooth Datenübertragung und die Rechenzeit für das Kugelpanorama (Stitching)

Resümee Realisierung #1

Für meine Anwendung am Fahrrad ist diese Realisierung deutlich zu langsam.

Realisierung #2: Gear 360 Modding

Mit einem Mod wird die intervallfunktion auf der Kamera selbst implementiert. Der Mod biegt die normale Foto-Funktion - 1 Aufnahme nach Drücken des Auslösers - um in - periodische Aufnahmen nach Drücken des Auslösers. Der Mod basiert auf die Arbeiten von ottokiksmaler und kann hier runtergeladen werden.

Der Entfall der Datenübertragung beschleunigt den Prozess der Bildaufnahme. Die minimale Intervallzeit für eine konstante Periodendauer beträgt 1,9s.

Resümee Realisierung #2

Für meine Anwendung am Fahrrad ist auch diese Realisierung zu langsam.

Realisierung #3: Time Lapse Video

Diese Funktion ist in der Kamera implementiert. Die kleinste einstellbare Intervallzeit ist 0,5s. Die Ausgabe ist ein Video mit 4096x2048 Px und 10 Bildern/s. Jedes Frame zeigt das Bild der beiden 180°-Linsen nebeneinander:

Bild 03: Frame eines Time Lapse Videos (Zoom). Linkes und rechtes Bild schauen jeweils quer zur Fahrrichtung

Die tatsächliche Intervallzeit beträgt jedoch 1,1s. Bei Einstellung “Time lapse interval” = 1s ist die tatsächliche Intervallzeit 1,6s usw. Das gilt ebenso für kleinere Videoauflösungen. An der Kamera stellt man offenbar eine Pausenzeit ein und nicht die Intervallzeit.

Die Frames haben weder Zeitstempel noch GPS-Koordinaten.

Resümee Realisierung #3

Eine Intervallzeit von 1,1s liefert bis zu 15 km/h einigermaßen flüssige Bildsequenzen. Für meine Anwendung am Fahrrad ist diese Realisierung ausreichend. Die nötige, intensive Nachbereitung der Daten kann man als Nachteil oder als Herausforderung sehen. Für mich ist es das letztere :-)

Datenaufnahme für #3

1. Gear 360 (2017) Kameraeinstellungen: Time Lapse Video; Auflösung 4k; Intervall 0.5s
2. GPS-Tracker auf 1s Intervall stellen
3. Video Aufzeichnung starten und GPS-Zeit (mindestens 1s Auflösung) filmen
4. Strecke abfahren
5. GPS-Zeit filmen und Video Aufzeichnung beenden

Die gefilmte GPS-Zeit ist für die Berechnung der Frame-Zeitstempel essentiell. Mein Garmin Oregon 650 zeigt die Sekunden in der GPS-Zeit nur in einem großen Datenfeld.

Datennachbereitung für #3

Ein vollständige Automatisierung ist leider nicht möglich, das Ablesen der GPS-Zeit vom Foto ist ein manueller Zwischenschritt. Alle anderen Schritte sind mit einem Windows Batch-File automatisiert.

Folgende Programmkomponenten müssen installiert sein (Windows):

1. FFmpeg (V 2025-09-18) Video Tool
2. ExifTool (V 13.12) Meta Information Editor
3. Hugin (V 2025.0.0) Panorama Stitcher
4. multiblend (V 2.0rc5) Fast Image Blender
5. gear360pano.cmd + gear360video4096.pto (V 2019-02-03) Script generiert Kugelpanorama. Das hier verlinkte Original benötigte 4 Anpassungen.

Schritt 1: Video zu Einzelbilder

Die Einzelbilder sind im Video als I- und B-Frames kodiert. P-Frames verwendet die Kamera Gear 360 (2017) nicht. Die Benennung der Bilder spiegelt die Position im Video. Beispiel für den Extrakt von I-Frames:

ffmpeg -v quiet -stats -i SAM.MP4 -vf "https://wiki.openstreetmap.org/wiki/Tag:select='eq(pict_type,I)'" -vsync vfr -frame_pts true -q:v 2 -qmin 1 -qmax 2 2_Images\out-%%04d.jpg

Schritt 2: Erstes und letztes Bild datieren

Um eine Sequenz von Bildern mit dem ExifTool zu datieren, ist der Startzeitpunkt des ersten Bildes und das zeitliche Inkrement zum nächsten Bild anzugeben. Die gefilmte GPS-Zeit am Anfang und Ende des Videos ist für das jeweilige Frame der Zeitstempel. Daraus lässt sich unschwer der Startzeitpunkt und das Inkrement berechnen. Ein Excel-File unterstützt dabei.

Das Ergebnis ist in das Script des nächsten Schrittes zu übertragen.

Schritt 3: Alle Bilder datieren

Zuerst werden alle Bilder mit den Startzeitpunkt datiert und anschließend werden die Sekunden mit dem Produkt aus Bild-Nr und Inkrement (in sek) erhöht.

exiftool https://wiki.openstreetmap.org/wiki/Tag:-datetimeoriginal=%starttime% -overwrite_original -progress: 2_Images

exiftool -fileOrder filename "-datetimeoriginal+<0:0:${filesequence;$_*= %incrtime%}" -overwrite_original -progress: 2_Images

Schritt 4: Kugelpanorama berechnen

Die Berechnung eines Kugelpanoramas aus zwei 180° Fischaugen-Bildern ist komplex und dauert immens lang. Pro Bild rechnet Hugin etwa 5s, d.h. ein Video mit 20min Länge braucht etwa 1h 31min zur Panorama berechnung.

call "gear360pano.cmd" /m /o "3_Stitched" "2_Images\*.jpg" "gear360video4096_V5.pto"

gear360pano.cmd ist eine leicht modifizierte Version von ultramango. Im Kern bildet es eine Schleife, die durch alle Bilder im angegebenen Verzeichnis geht.

gear360video4096_V5.pto ist eine Vorlage für Hugin, die alle Parameter zur Berechnung des Kugelpanoramas enthält. Ich machte meine eigene Vorlage, da mir die Version von ultramango zu unpräzise zusammenfügte.

Bild 04: Kugelpanorama mit äquirektangulärer Projektion (zoom) aus Bild 03. Siehe Mapillary für eine Darstellung im 3D-Raum.

Schritt 5: Geotagging der Bilder

Die Position des Bildes wird über den Abgleich der Zeitstempel in den Bildmetadaten (EXIF) mit den Zeitpunkten der GPS-Aufzeichnung bestimmt.

exiftool -geotag TRK.gpx -overwrite_original -progress: 3_Stitched

Resümee

Mit der Kamera Gear 360 (2017) kann mit minimalen finanziellen Aufwand eine funktionierende 360° Street View -Aufnahmelösung aufgebaut werden. Das Ergebnis sind Bilder die unmittelbar auf Mapillary hochgeladen werden können. Die Bildauflösung ist für räumliche Strukturen ausreichend, für Beschriftungen aber oft unzureichend (z.B. hier).

Mit der Samsung App beträgt die minimale Intervallzeit 3s. Für Street View-Aufnahmen als Fußgänger ist diese Konfiguration optimal, weil sie kein Postprocessing der Daten benötigt.

Radfahrer wählen besser den Weg über das Time Lapse-Video. Hier beträgt die minimale Intervallzeit 1,1s. Das reicht für Street View-Aufnahmen bis etwa 15 km/h. Diese Konfiguration erfordert ein intensives Postprocessing der Daten. Bis auf das Ablesen der Uhrzeit sind jedoch alle Schritte automatisierbar.

Die Batch-Files können von GitHub heruntergeladen werden. Sie laufen unter Windows, sollten aber auch für andere Betriebssysteme und andere 360°-Kameras angepasst werden können.

contributors

may I point you to our online mapathon tomorrow night?

Missing Maps London Online: Zoom

6th January 19:00 – 21:15 (Europe/London)

The early month meeting is focused on new mappers with breakout rooms for iD, JOSM and mappers who want to learn about validation usually with JOSM. Next one is 6 January Get your free ticket .

This Mapathon will take place online! If you wish to attend, then please get a ticket and details for joining will be shared with you.

This event is London based in name only! Since we have moved to virtual events we have been getting attendees from all over the world - we heartily welcome this!

Schedule 19:00: Introduction to Missing Maps and practical information

19:15: iD Training / JOSM Training / Validator Training

21:00: Closing discussion and potential talks

Mismatched descriptions: a Categoria

Aqui e graças a uma extensão que instalei no Edge (que já não sei onde fui desencantar), que me permite ver as categorias ocultas das páginas da wiki e descobri mais coisas pra fazer. No caso mais ou menos simples, mas dentro de uma estrutura hiper mega complicada (claro); fui aos confins da por assim dizer wikidata da wiki do OSM (coisa que desconhecia… e que por ter feito esta empreitada manualmente através dos artigos da wiki nem posso dar link aqui)… E pôr as descrições dos artigos a coincidir com a descrição na wikidata porque pelos vistos isso é uma coisa. Divertido q.b., e assim também traduzi e completei algumas páginas.

Depois de cerca de 20 páginas aniquilidas…

Fontes de dados em Portugal: a tabela

Também não sei como descobri esta página incrível. Pus-me a ver se os links funcionam numa tabela infinita de links para todas as câmaras municipais do país que providenciam dados geográficos. Muito pouco claro se é possível usar esses dados no OSM, mesmo que esses mesmos sites usem os dados OSM. Assim rasurei um monte de links mortos (não, não apaguei)…

Pelo meio aprendi como fazer um template e criei um aviso na página… Porque demorou mesmo muito tempo e eu nem sequer cheguei a meio.

Depois vi páginas de perfil de utilizadores da wiki para perceber como é que hei-de criar uma para mim. Mais templates.

E assim porque há tanto código diferente em todas estas plataformas OSM related e por facilidade mental também me pus meio que a remar contra a corrente e a usar HTML aqui e ali?… Obrigada myspace!

Vou deixar aqui estes links, posso precisar deles:

• Data items

• Templates

• Guia HTML

• Guia kramdown

25/12/2025-31/12/2025

Community

• Carlos Felipe Castilo has developed a web dashboard to predict population figures in a region using OpenStreetMap data, such as building footprints, levels, and housing density.
• Evgeny Arbatov noted the importance of local knowledge from runners and walkers who discover unmapped streets.
• Rphyrin has conducted a field mapping expedition along the west coast of Java.
• Rtnf travelled to Bandung to attend a wedding, observing the surroundings during the journey and mapping them on OpenStreetMap.

Imports

• Chris Debian is proposing a bulk import of historical bunkers and pillboxes in the UK into OpenStreetMap.
• Damianeue is preparing a mass data import of Italy’s boundary data from the Database di Sintesi Nazionale into OpenStreetMap.

OpenStreetMap Foundation

• The OpenStreetMap Operations Team reported that they just received an exceptionally generous bitcoin donation of 2 BTC (valued around 177,017.60 USD at transaction time). You can also donate to OpenStreetMap using bitcoin.

Local chapter news

• Unique Mappers has released their 2025 annual newsletter.

OSM in action

• ^[1] The Ministry of Tourism of the Republic of Indonesia has developed the National Tourism System (Sistem Pariwisata Nasional / ‘Sisparnas’), an OpenStreetMap-based webmap that displays all officially registered tourism destinations across the country.

Software

• Victor and Eugene shared OsmAnd’s 2026 New Year’s resolutions, offering a glimpse of future updates and improvements.

Programming

• The OSM Apps Catalog is getting a new design thanks to a microgrant from the OSM Foundation. Markus, aka ToastHawaii, the author of the OSM Apps Catalog, has asked the community to participate in a survey to better understand what users need.

Other “geo” things

• Tyler August, of Hackaday, reported that Instructables user madkins9 has developed a globe for playing the game Risk, resolving the long-standing debate over the most preferable map projection for players.
• Lorenz Hurni and others have written Engineers of Map Art, a comprehensive overview of 170 years of cartography at ETH Zurich, published to mark the 100th anniversary of the Institute of Cartography and Geoinformation.
• The Japan Geospatial Times, by Eita Horishita, has published an article introducing Japan’s FOSS4G community, featuring OSGeo Japan and the OpenStreetMap Japan ►.
• Josh Meissner examined the uneasy relationship between corporate capital and community-powered services, through the recent sale of the route-planning platform Komoot to private equity (we reported earlier).

Upcoming Events

Country	Where	Venue	What	When
	नई दिल्ली	Jitsi Meet (online)	OSM India – Monthly Online Mapathon	2026-01-04
			Missing Maps London: (Online) Mapathon [eng]	2026-01-06
			iD Community Chat	2026-01-07
	Stuttgart	Stuttgart	Stuttgarter OpenStreetMap-Treffen	2026-01-07
			MapYourGrid webinar: Data quality in OpenStreetMap for energy system planning	2026-01-08
	Berlin	Restaurant Neumanns	211. OSM-Stammtisch Berlin-Brandenburg	2026-01-08
		Online	OpenStreetMap Midwest Meetup	2026-01-08
	Dresden	Bottoms Up, Dresden	OSM-Stammisch Dresden	2026-01-08
	Bochum	Das Labor, Alleestraße 50, Bochum	OSM-Treffen in Bochum	2026-01-08
			OSMF Engineering Working Group meeting	2026-01-09
		Jitsi-Meet	Erstes Online-Treffen der OSM-Mapper:innen im Sauerland	2026-01-09
	Zürich	Bitwäscherei Zürich	183. OSM-Stammtisch Zürich	2026-01-09
		Chiasso	Mapping party @ New Year’s brunch by Wikimedia CH	2026-01-10
	New Delhi	Indian Coffee House, Connaught Place	OSM Delhi Mapping Party No.25 (Central Zone)	2026-01-11
	København	Cafe Bevar’s	OSMmapperCPH	2026-01-11
			Missing Maps : Mapathon en ligne – CartONG [fr]	2026-01-12
	臺北市	MozSpace Taipei	OpenStreetMap x Wikidata Taipei #84	2026-01-12
	München	Echardinger Einkehr	Münchner OSM-Treffen	2026-01-13
	Mangaluru		Mapping Party @ Surathkal	2026-01-18

Note:
If you like to see your event here, please put it into the OSM calendar. Only data which is there, will appear in weeklyOSM.

This weeklyOSM was produced by MatthiasMatthias, Raquel Dezidério Souto, Andrew Davidson, barefootstache, miurahr.
We welcome link suggestions for the next issue via this form and look forward to your contributions.

কাতিলা শষ্ঠীতলা মন্দিরটি সঠিক অবস্থান ও মানসম্মত OSM ট্যাগ ব্যবহার করে ম্যাপে যুক্ত করা হয়েছে। মন্দিরটি রাজশাহী জেলার বাগমারা উপজেলার যোগীপাড়া ইউনিয়নের কাতিলা গ্রামে অবস্থিত। নিকটবর্তী পরিচিত বাজার হিসেবে নাগা বাজার উল্লেখযোগ্য, যা মন্দির থেকে আনুমানিক ১.৪ কিলোমিটার দূরে।

Hoje passei uma dúzia (!) de horas a tentar resolver a página “Pt:Map features”… E… não consegui fazer nada de jeito (acho até que piorei a situação). Fiquei cansada de ver “aerialways” (o primeiro item da lista) o dia todo… Terrível!! Foi esta página que me entreteve a necessidade de perceber alguma coisa que fosse.

Esta wikipédia tem um problema grave com os templates. Entre traduzir, fazer o cross referencing, aprender nem sei que código… E subpáginas??? E erros de JSON?! e tabelas a dar erros com letras a vermelho….!! E… Secções de artigos separadas aleatoriamente por partes de templates sem nexo nenhum…

Uf. Seria muito mais fácil se realmente se usassem as tabelas que vêm com as páginas de template, assim a tradução seria praticamente automática (já quase que é, na verdade) e seria mais fácil editar alguma coisa assim à primeira. Hei-de voltar a ocupar-me com esta tarefa hiper mega complicada.

Fora isso estive a ver o que mais podia sintetizar ou traduzir nas várias Pt:isto ou aquilo, e assim fiz num nível muito básico. Acabei a ver propostas, e vieram-me algumas à ideia.

Muito trabalho a fazer. O que é.. bom! E pronto, amanhã será outra coisa qualquer.

I’m finally back to mapping after an unplanned 4-month break.

I usually have a casual but relatively consistent approach to mapping: every time I have to go somewhere new and my exact destination isn’t in OSM, I add it once I arrive; if the opening times of a place I visit regularly change, I update them, and so on. As of late, though, this just hasn’t happened. Either map data has become good enough for my everyday needs in Gothenburg, Sweden or I’ve been paying less attention than usual. Probably a little bit of both.

Right now, on the other hand, I’m visiting my family in Agrigento, Italy, and mapping is the perfect holiday activity for me: it can (to some extent) be done without a computer, which is a nice change of pace, and it gives me an excuse to be outisde, enjoy the warm weather and take all the side streets I haven’t explored before or have forgotten about. Plus, there’s so much to do here compared to Gothenburg!

Today I simply went for a walk and answered a bunchn of quick questions on StreetComplete. In the next few days, I want to try something more systematic, like updating information about what’s happening inside each of the buildings of the main street: a lot has changed since I last came. I’m also pondering trying to get one or two family members involved; we’ll see.