Pour la Journée Mondiale de l’Eau ce 22 mars 2025, je vous ai concocté quelques requêtes Overpass qui valorisent le modèle de données OSM décrivant les cours d’eau. Ce modèle est décrit sur cette page du wiki, la clef qui nous intéresse ici est waterway.

Les cours d’eau d’une commune

Commençons par une requête classique visant à extraire les cours d’eau se trouvant à l’intérieur d’un territoire. Prenons la commune de Nort-sur-Erdre en Loire-Atlantique, la rivière L’Erdre présentant des caractéristiques intéressantes pour la suite de cet article. Notre première requête produit tous les cours d’eau se trouvant à l’intérieur de la commune. Pour vérifier le résultat (une bonne pratique avec Overpass), la requête retourne également le contour de la commune.

Les 3 lignes de MapCSS à la suite de la requête permettent de styliser l’affichage du résultat. La clause render: native; indique d’afficher tous les tronçons sous forme de lignes, même les plus courts qui sont par défaut représentés par des cercles selon le niveau de zoom.

On observe deux problèmes :

1. Les portions de cours d’eau qui longent le périmètre sont manquantes, notamment au niveau de la Plaine de la Poupinière au sud-est de la commune.
2. Le résultat contient des ways qui sortent du périmètre de la commune, à l’ouest et au sud.

Pour résoudre le premier problème, nous devons prendre en compte à la fois les cours d’eau se trouvant à l’intérieur de la commune avec le filtre (area), et ceux le long de son périmètre avec le filtre (around). Celui-ci s’applique à des ways : on récupère les ways référencés par la relation avec la syntaxe way(r.commune). Notre seconde requête utilise une distance nulle, il est aussi possible d’utiliser une distance de quelques mètres pour récupérer des cours d’eau proches de la commune.

Pour le second problème, il est possible de réaliser un clipping des rivières par le contour de la commune, en récupérant les seuls nodes des ways se trouvant à l’intérieur de la commune : c’est le rôle de l’instruction node(w)(area.a); dans notre troisième requête. Les ways incomplets (dont une partie des nodes n’est pas retournée par la requête) sont affichés en pointillés bleus.

Nous pouvons observer au sud de la commune deux cours d’eau parallèles très proches l’un de l’autre. Il s’agit du Canal de Nantes à Brest (waterway=canal) et la rigole qui le longe (waterway=ditch). Si nous nous intéressons uniquement aux cours d’eau naturels, il convient d’utiliser les valeurs river, stream et flow_line pour le tag waterway. Cette dernière valeur, qui décrit l’écoulement dans un plan d’eau, a été définie courant 2024 et est encore peu présente dans les données. Nous utilisons l’expression régulière ^(river|stream|flowline)$ pour filtrer ces seuls éléments. Pour plus de clarté notre requête « aboutie » ne retourne plus la périmètre de la commune.

Les cours d’eau en amont d’un point

Le bassin versant est la zone de collecte des eaux en amont d’un point. Son polygone se calcule à partir d’un Modèle Numérique de Terrain. Il est possible, avec OpenStreetMap, de produire le réseau hydrographique d’un bassin versant.

Partons du way traversant la Plaine de la Poupinière. Les sections de cours d’eau directement en amont terminent sur ce way : leur dernier node est un des nodes de ce way. On récupère donc les nodes de notre way de départ avec node(w), puis les ways de type waterway dont le dernier node est un de ces nodes, avec l’instruction way(bn:-1)[waterway];. On place cela dans une boucle complete, qui se termine quand le résultat n’évolue plus. Notre requête ne fait que quelques lignes !

Pour partir d’un point plutôt que d’un way OSM, nous utilisons le filtre (around) avec 2 variables Overpass Turbo, définies par des accolades. L’une définit le point de départ, en aval du bassin versant, l’autre la distance maximale entre le point fourni et le cours d’eau le plus proche. Cette requête peut ainsi être facilement adaptée à votre besoin.

À partir de ce résultat, il est possible d’identifier les communes traversées par les cours d’eau en amont de notre point. L’instruction is_in, qui s’applique à des nodes, permet de trouver les éléments de type area qui les contiennent. Nous filtrons ce résultat pour ne retenir que les communes avec cette requête.

Nous pouvons également produire, à la place d’une carte, la liste des cours d’eau du bassin versant, avec leur nom et leur longueur totale. Nous utilisons une boucle sur les valeurs du tag name, l’instruction make qui permet d’utiliser la fonction d’agrégation sum() sur l’évaluateur length() qui calcule la longueur d’un way. Cette requête ne produit pas de carte mais un résultat textuel au format CSV.

De la source à la mer

Après avoir remonté les cours d’eau, suivons les jusqu’à leur embouchure. Partons de la source de l’Erdre, puis prenons un tronçon connecté à cette source. La démarche consiste ensuite à prendre le node à la fin de ce way, pour trouver les ways connectés à ce node. Nous utilisons à nouveau une boucle complete, en prenant soin de ne pas remonter les affluents rencontrés en cours de navigation, c’est -à-dire les cours d’eau qui se terminent sur les ways trouvés jusqu’ici. La requête utilise donc une soustraction à l’intérieur de la boucle complete.

Les cours d’eau large comme la partie aval de l’Erdre et la Loire sont aussi représentés par leur surface, avec le tag natural=water. On peut obtenir les plans d’eau traversés par une goutte d’eau s’écoulant de la source de l’Erdre jusqu’à l’Océan Atlantique. Cette requête retourne le cours d’eau linéaire ainsi que les plans d’eau traversés. Ceux-ci sont retournés dans leur entièreté : on récupère donc toute une portion de la Loire en amont du confluent avec l’Erdre.

Pour terminer intéressons-nous aux zones humides (natural=wetland), nombreuses le long de l’Erdre et de la Loire. Nous utilisons à nouveau une boucle complete pour obtenir les zones humides directement adjacentes aux plans d’eau, ainsi que celles plus en profondeur dans les terres. Notre dernière requête produit des données un peu conséquentes, mais le résultat est intéressant.

Lots of people use editors such as iD, Potlatch, Vespucci, GoMap!! etc. for editing. There are entirely sensible reasons for this - I’ll always try and edit relations in Potlatch or iD since for me editing relations there is a much saner experience than in Josm. However, one thing that they miss is Josm’s Validator, which can check for relation errors that other editors can’t. Here’s how to use that to detect problems, and then fix them elsewhere.

I’ve created some test data on the dev server for this, so that I can deliberately create and fix errors. If you want to test with that data on the “dev” server, you’ll need to create an account there and tell Josm to login to that server - or you can just look at the screenshots below.

First, you’ll need to download Josm (I just downloaded the latest .jar file) . Josm’s user interface will be familiar to anyone who used CAD software in the 1980s, but may be less so to others.

Then you’ll need to download some data in the area that you were editing (file / download data / download). So that you can see what is where, it helps to have a background layer - “OpenStreetMap Carto (standard)” will work, or you can use an imagery layer if you prefer. Zoom in to your area of interest, select with the mouse and “download”.

Then click “validate” (on the row at the very bottom right of the screen).

Click on the “+” sign, and then on the second plus sign that has appeared, and then right-click on the actual error and “zoom to problem”. Josm highlights that the relation has two members that corss over each other, in a figure of 8. In OSM that looks like this:

What we need to do is to move the place where the relation is attached to the canal a bit north so that all three relations are valid. It’s a simple fix, so you can do that in any editor that you are comfortable with. I’ll do it in iD

Finally, we need to use Josm’s validator to make sure that nothing else is now an error. It’s the same process as before (File / download data / download, and then “validate”). Now we just have warnings:

The “crossing boundaries” warning (note that it is now not an error) is saying that an area of this relation and this relation now overlap. It’s correct, but fix that properly we’d want to edit the data so that no relations used the bridge at all, which is out of scope of what I’m trying to write here.

The reason why I used an example in Ireland is that Ireland has lots of relations (such as townlands) defined in terms of other features, such as roads and rivers, and except where there is a ford, in OSM roads and rivers shouldn’t join, because one is on a layer above the other. The historical administrators in Ireland didn’t anticipate this, and defined lots of boundaries as “follow this road to this river, then go up that”. The database that I create raster maps from has these relations in it, and I’ve set it up to be notified when any of them “disappear” as valid (multi)polygons.

We’re happy to announce that we now have a nice public transport map, courtesy of Melchior Moos who runs the original öpnvkarte.de web site. Geofabrik now hosts its own version of that original “ÖPNVKarte” (“ÖPNV” is the German acronym for public transport), and we’ll be offering it to international customers under the somewhat simpler name “Public Transport Map”.

We’ll also approach the OpenStreetMap operations team and recommend this map for inclusion on www.openstreetmap.org which has featured ÖPNVKarte for a long time and only recently dropped it due to operational concerns.

Teach GIS after school at High Point Library Work other PH content into mix with ongoing GIS training

benefits #external #internal

risks #internal #external

This blog post is part of a series of blog posts about the new OSM file format “OMA”. This is the third post. At the end of the article you’ll find links to the other blog entries.

Until now you’ve got a general idea of what the Oma file format is, and an idea of how to use it. But you do not know, where to get an Oma file from.

Well, I hope, that sooner or later someone like Geofabrik will provide a daily updated planet.oma and some excerpts. That would make sense, because converting the data takes a lot of resources, and it would be a waste if everyone had to do it themselves.

But until we have such a distributor, you have to convert OSM files to Oma files on yourself. I have written a converter for this purpose. It’s written in Java and should be easy to use.

The Converter

You need a copy of oma.jar. If your are using Linux (or any other Unix operating system) you just have to type the following command:¹

java -Xmx<some number>G -jar oma.jar <some osm file>

The -Xmx part tells the Java Virtual Machine to use <some number> gigabytes of memory. For example, my computer has got 4GB of main memory, so I’ll use -Xmx3G, reducing the available memory by 1GB, because the operating system needs some memory too.

The osm file mentioned in the command, can be one of .osm, .o5m or .pbf².

Well, that’s about it. The program will read the file and start the conversion. This can take a long time, and hopefully it won’t crash.

Huh, crash? I wish I could give you better news, but unfortunately I have not been able to write a program that does never crash. The reason for this is that Java gives no guarantees or means of dealing with out-of-memory situations.³ So: If you have enough memory (and disk space), a crash should never happen, but if you have only limited memory, a crash might be possible.⁴

If the program crashes: Try restarting your computer (this removes memory segmentation) and/or using less(!) memory. In this case it’s also a good ideas to stop doing anything else on the computer while the conversion process is running.

There are some options you can pass to the program. For example, you can use -p to tell the program which meta information to include in the output file. Type java -jar oma.jar --help to get a complete list.

I recommend to try the program with smaller files first, using the -v switch, to get a feeling for what it does.

Beneath the Surface

I’ll end this post with a brief description of what happens during the conversion.

Conversion is done in three steps.

The first step reads the input file and splits the data into five parts: nodes, ways, ways created from relations, areas created from relations and collections created from relations. Next, it replaces most of the node and way references with coordinates of the nodes and ways, and finally it merges everything together, making some more changes related to relations. I’ll go into more detail on this in a separate post on relations.

The second step reorders the elements in the file: Each element is put into a geographically bounded chunk.

The third step analyses the elements and tries to determine the type of the element (highway, landuse, building, etc.) In the case of ways, it uses this type to determine whether the element is a real way or an area. It also uses this information to sort the elements once more, this time into blocks and slices.

If you are curious about the inner workings of the converter, try using the -v flag three or four times. You’ll get lots of messages about what’s going on during the conversion.

See also

• A New File Format for OSM Data
• Using the Oma Library

1. I’m not familiar with other operating systems. Since Java is independent of the operating system, chances are good, that it will work with other operating systems too, and I guess that the call wouldn’t be much different. ↩

2. Reading pbf files is a bit experimental. In particular, some features of this format are not implemented because I couldn’t find any examples. In practice, it worked whenever I used it. ↩

3. According to the specs, Java’s OutOfMemoryError can happen at any time. In practice, it usually only happens, when you try to allocate new memory. You would like to know ahead of allocation, if it will work. Unfortunately, Java doesn’t provide a way to tell you. You could query the total amount of memory available, but even if it’s larger than the amount you need, that doen’t mean, that the allocation won’t fail (due to memory segmentation). Next you’d expect, that if the allocation would fail, Java would start the garbage collector, before giving up. Nope, false hope. Java doesn’t do that. Even, if you start the garbage collector yourself, there’s no guarantee that it will run. (Fortunately, in practice it does. So I’m using this, but it slows everything down.) If you’ve read this far and you’ve got an idea on how to improve this, I’d love to hear it! ↩

4. Disk space is intentionally not checked. Make sure you have enough before running the program. ↩

– Read in Inglês

Acessando informações em momentos de crise - Entrevista com a Dra. Raquel Dezidério Souto, sobre o desastre do Rio Grande do Sul (Brasil), ocorrido em abril e maio de 2024

Esta entrevista está registrada no Zenodo.org e disponível como arquivo PDF. Como citar esta entrevista:

Acessando informações em momentos de crise: entrevista com a Dra. Raquel Dezidério Souto, sobre o desastre do Rio Grande do Sul (Brasil), ocorrido em abril e maio de 2024. Entrevistada: Raquel Dezidério Souto. Entrevistadora: Laura Bortoloni. Rio de Janeiro: IVIDES.org, 20 mar.2025. DOI: https://doi.org/10.5281/zenodo.15058822. Licenciado sob CC-BY-NC-ND 4.0 Ⓒ autoras.

Esta entrevista está disponível também em Inglês: https://doi.org/10.5281/zenodo.15058928

1. Perfil profissional

Você pode nos contar sobre o seu background e como se envolveu com a cartografia?

Meu primeiro contato com a cartografia foi na graduação em oceanografia. Depois, no mestrado em Estudos Populacionais e Pesquisas Sociais (IBGE) e no doutorado em geografia (UFRJ). Ao longo do tempo, desenvolvi linhas de pesquisa em mapeamento colaborativo, com apoio de cartografia digital e mapeamento Web. O foco do meu pós-doutorado em geografia tem sido o desenvolvimento de soluções Web para mapeamento colaborativo digital. Atualmente, desenvolvemos projetos com software livre ou projetos híbridos (misturando software livre e proprietário), no âmbito do Instituto Virtual para o Desenvolvimento Sustentável - IVIDES.orgⓇ, instituto virtual de pesquisas que criei em 2008; alguns desses projetos, sendo viabilizados pela IVIDES DATA, sua empresa gestora.

O que a atraiu para a cartografia humanitária e para os projetos de cartografia participativa?

Ainda em 2019, nós desenvolvemos o nosso primeiro mapa colaborativo, quando do desastre do petróleo, que atingiu pouco mais de 50% de toda a costa brasileira (que tem um total de cerca de 8,5 mil km). Dada a extensão da costa e a emergência do evento, nós colocamos à disposição do público, uma plataforma com as localidades oleadas e as fotografias que eram enviadas pelo grupo de WhatsAppⓇ. Nesta época, nós não conhecíamos ainda o OpenStreetMapⓇ e construímos o mapa Web no Google My MapsⓇ https://ivides.org/mapa-participativo-petroleo-2019-2020. Conhecemos o projeto OpenStreetMapⓇ em 2021, em um curso sobre Leaflet https://leafletjs.com/, que foi oferecido pela Universidade do Estado do Rio de Janeiro. Com ele, desenvolvemos um mapa interativo durante a Pandemia de COVID-19 https://ivides.org/mapa-dinamico-de-incidencia-de-covid-19, que mostrava os dados fornecidos pelas prefeituras dos municípios do Norte e Noroeste do Rio de Janeiro (Brasil). E a campanha pelo Rio Grande do Sul 2024, que detalharei adiante. Depois, com a formação nos cursos oferecidos pelo UN Mappers https://mappers.un.org/learning/, conhecemos o universo do mapeamento humanitário com OSM. Com a formação, passei a integrar o grupo de mapeadores validadores do UN Mappers, que agregou mais experiências práticas de mapeamento em diversos outros países. A adesão ao YouthMappers, como um capítulo para a Universidade Federal do Rio de Janeiro (Brasil), à qual estou afiliada, proporcionou (e proporciona ainda) a transferência deste conhecimento ao público acadêmico e a pessoas externas à universidade, tendo promovido o treinamento em mapeamento com OpenStreetMapⓇ, de cerca de 700 pessoas, nos últimos dois anos.

** Já trabalhou anteriormente em projetos de cartografia de resposta a catástrofes semelhantes? Em caso afirmativo, como é que esta experiência se compara?**

Sim, sempre que possível, nos projetos promovidos pelos grupos de usuários do OSM, pelo UN Mappers, pelos capítulos YouthMappers brasileiros e por alguns capítulos da África, especialmente, de Moçambique e Angola. O último projeto de mapeamento colaborativo do qual participei, foi (está sendo) a resposta ao desastre provocado pelo ciclone Chido, na Ilha Mayotte, um território ultramarino francês, adjacente à costa de Moçambique, no Oceano Índico - https://www.openstreetmap.org/relation/3388394#map=5/-15.96/50.12. Os projetos estão disponíveis em https://tasks.hotosm.org/explore?text=Mayotte&omitMapResults=1.

A realização de mapeamento colaborativo é bem diferente quando adotamos dados abertos e plataformas livres, como os gerenciadores de tarefas (tasking managers) e aplicativos para celular, de código aberto e editável por outros. As soluções proprietárias limitam a personalização dos projetos (do pontos de vista computacional) e impedem o compartilhamento de dados e informações, o que prejudica a interoperabilidade dos dados entre diferentes sistemas. A abertura de dados e a interoperabilidade são cruciais em momentos desastrosos, que exigem uma resposta rápida daqueles que lidam com o evento, durante e após a sua ocorrência. Assim, atualmente, adotamos soluções livres na resposta aos desastres e temos capacitado as novas gerações, para que estejam aptas a participar dos projetos existentes e propor (e gerenciar) novos projetos de mapeamento colaborativo humanitário.

2. Enchentes no RS

Como se envolveu no projeto de cartografia colaborativa das cheias do Rio Grande do Sul?

Eu soube do desastre por familiares residentes em Canoas, na região do Guaíba, no Rio Grande do Sul (eu resido no Rio de Janeiro). Meus tios perderam a casa e todos os pertences, após o rompimento da barragem de água, que fica a montante do rio. Depois, começaram os comentários sobre o desastre nas mensagens do grupo OSM RS no Telegram, grupo de usuários do OpenStreetMap nesse estado, e pelo Humanitarian OpenStreetMap Team (HOT), que disponibilizou diversos projetos no HOT Tasking Manager (HOT-TM). Na época, estávamos com uma agenda de treinamentos em mapeamento temático com OSM, promovida pelo IVIDES.orgⓇ, e criamos um projeto de mapeamento colaborativo no gestor de tarefas, a fim de mapear a bacia hidrográfica Taquari-Antas https://tasks.hotosm.org/projects/16706, a região mais afetada pela tragédia. Este projeto também serviu de apoio para a realização da oficina de mapeamento de cursos d’água (waterways no OSM), ministrada por Séverin Ménard (Les Libres Géographes, LLg), na época, consultor para o United Nations Global Service Centre (UNGSC). A escolha da bacia hidrográfica como área de interesse (AOI) do projeto justifica-se na importância em avaliar a ocorrência do evento em uma divisão geográfica e não político-administrativa, pela natureza do fenômeno (desastre hidrológico).

Qual foi o papel específico do Humanitarian OpenStreetMap nesta emergência?

HOT é uma entidade sem fins lucrativos estadunidense, que provê recursos humanos e materiais para a gestão de projetos e dados abertos, o que facilita as atividades de mapeamento colaborativo, durante e após a ocorrência dos desastres. A adoção do gestor de tarefas (tasking manager) tem crescido ao longo do tempo e essa solução, que também está sendo utilizada em projetos de outros organismos internacionais e tem código aberto https://github.com/hotosm/tasking-manager, permite aumentar a velocidade do mapeamento, um aspecto fundamental, já que o tempo é algo crucial na resposta a emergências. No entanto, ainda enfrentamos dificuldades em obter imagens aéreas com ótima resolução e atualizadas no Brasil; e ainda não estão bem claros no nosso país, o papel da organização e a utilidade ou aplicação dos dados que são gerados, o que poderá ser resolvido com maiores campanhas de esclarecimento junto à população, em geral, e agentes públicos, e com a continuidade da promoção de cursos e outras capacitações.

Pode descrever o contexto em que esta iniciativa foi lançada e a forma como foi coordenada com as autoridades locais e as ONG?

O desastre do Rio Grande do Sul envolveu eventos de movimentos de massa, enchentes, inundações, alagamento e erosão fluvial, que ocorreram nos meses de maio e abril de 2024, mas com efeitos que perduram até hoje. Segundo relatado na página da Wikipedia, criada para documentar o desastre:

Em várias cidades, no período entre 27 de abril e 2 de maio, chegou a chover de 500 a 700 mm, correspondendo a um terço da média histórica de precipitação para todo um ano, e em muitas outras a precipitação ficou entre 300 e 400 mm entre 3 e 5 de maio. (...)  Dados do Instituto de Pesquisas Hidráulicas (IPH), da Universidade Federal do Rio Grande do Sul (UFRGS), mostram que as chuvas de maio levaram mais de 14 trilhões de litros de água para o lago Guaíba, volume que equivale a quase metade do reservatório da Usina Hidrelétrica de Itaipu. (...) A precipitação excessiva afetou mais de 60% do território estadual. 

Inicialmente, os projetos disponibilizados no HOT-TM não foram coordenados com autoridades locais, pois a maioria das prefeituras de municípios atingidos pelo desastre adotaram soluções proprietárias para mapeamento. Mas foi estabelecida uma ligação com o Ministério da Integração e Desenvolvimento Regional (MIDR) do Brasil, a fim de conhecer os locais mais afetados. Por exemplo, a Prefeitura de Porto Alegre adotou as mesmas soluções que já eram utilizadas pela Defesa Civil do Estado de São Paulo, que se baseiam em softwares proprietários. Infelizmente, estas administrações não reconhecem a importância dos aplicativos e dados abertos, mesmo sendo claras as vantagens de sua utilização, reconhecidas em diversos artigos científicos publicados na Europa, sobre relatos de casos de sucesso na gestão dos riscos e enfrentamento de consequências dos desastres.

Estes programas livres, associados à utilização da base cartográfica do OSM poderiam melhorar o atendimento e socorro das vítimas, além de economizar recursos públicos, uma vez que não há pagamento por licenças de uso. Cabe lembrar que a mesma Prefeitura de Porto Alegre realizou cooperação posterior para a avaliação dos prejuízos causados às edificações e vias, a partir dos dados mapeados por todos no HOT-TM, mas, infelizmente, esta avaliação não envolveu amplamente a comunidade de mapeadores, tendo sido realizada apenas entre a ONG internacional, o governo local e o Banco Interamericano de Desenvolvimento (BID), com o propósito de estimar os valores necessários para as obras de reconstrução.

3. Processos de mapeamento

Como foi organizado o trabalho entre voluntários e profissionais?

No nosso projeto de mapeamento, a gestão dos participantes foi toda realizada por meio do gestor de tarefas e dos meios sociais de comunicação - grupo de WhatsAppⓇ e grupo de e-mail. O mapeamento das feições, com prioridade inicial para edificações e vias, foi realizado por pessoas de qualquer nível de conhecimento, a validação foi (e ainda está sendo) realizada por mapeadores com níveis intermediário ou avançado (o que corresponde a 250 ou 500 conjuntos de dados enviados, respectivamente). Essa divisão é importante para aumentar a qualidade do mapeamento. A interação nos grupos é fundamental para a solução de dúvidas e problemas e para a troca de dados e informações. Para chamada à participação nas atividades de mapeamento colaborativo (maratonas de mapeamento) e para a comunicação dos resultados, contamos ainda com a publicação de artigos curtos no Semanário OSM (weeklyOSM), que tem distribuição mundial.

Houve algum desafio específico na coleta de dados ou no envolvimento da comunidade?

No Brasil, há dificuldades para aquisição de imagens aéreas com alta resolução, suficientes para mapear adequadamente as edificações, nas fases pré, durante e pós-desastre. Outro empecilho tem sido a adoção de licenças fechadas (ou a ausência de declaração do tipo de licença) nos conjuntos de dados oficiais. Algumas prefeituras têm realizado a celebração de acordos, em que a empresa prestadora de serviços atua como proprietária dos dados, quando, pela lei brasileira, estes dados necessitam estar disponíveis publicamente (Lei de acesso à informação, Lei n. 12.527/2011, https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2011/lei/l12527.htm). Muitas pessoas no Brasil possuem poucos recursos (como bons computadores, celulares e acesso à Internet, por meio de redes de alta velocidade), especialmente, nas regiões Norte e Nordeste do Brasil, o que limita a sua participação. A falta de capacitação para operar os programas também é um limitador para a realização das mapatonas (maratonas de mapeamento) e temos trabalhado no IVIDES.orgⓇ, no sentido da capacitação de novos(as) mapeadores(as) para os projetos de mapeamento colaborativo.

4. Participação comunitária

Que estratégias você usou para tornar o mapa acessível e compreensível para os afetados pela enchente?

Nós promovemos alguns eventos públicos, para engajar a população nos projetos de mapeamento colaborativo. Um dos eventos foi citado anteriormente, a oficina de mapeamento temático de cursos d’água (waterways) e feições relacionadas no OpenStreetMap. O outro evento foi o Seminário Científico pelo Rio Grande do Sul, https://ivides.org/seminario-rs, onde pesquisadores de universidades públicas localizadas no estado (FURG, UFRGS e UERGS), que realizaram ações relacionadas ao enfrentamento das consequências do desastre, puderam mostrar seus mapeamentos, trocar informações e discutir as dificuldades encontradas.

Os eventos foram realizados remotamente, para maior alcance do público e os vídeos foram disponibilizados na Wikimedia e no canal IVIDES.orgⓇ no YouTubeⓇ (em dois momentos: https://www.youtube.com/live/fD5kp_j6w_Y e https://www.youtube.com/live/Dv9t2ZTRsWQ), chegando a milhares de visualizações. A página do projeto no nosso portal institucional também ajudou a disseminar essa iniciativa e promoveu o acesso público aos dados oficiais que conseguimos levantar na época, a partir de diversas fontes, https://ivides.org/desastre-rio-grande-do-sul-brasil-2024.

Você pode compartilhar um exemplo de como o mapeamento participativo teve um impacto direto nas operações de socorro?

Durante este mesmo evento desastroso, as vias e pontes que estavam interditadas ou destruídas foram mapeadas pela comunidade do OSM RS, especialmente, pelo mapeador Fernando Trebien (aka ftrebien), e estes dados fizeram parte do mapa Web que elaboramos com o uMap, https://umap.openstreetmap.fr/pt-br/map/situacao-vias-rs_1070918. Estes dados foram utilizados pelo Departamento Autônomo de Estradas de Rodagem (DAER-RS) para atualizar o seu próprio mapa de vias afetadas, apesar de não estar explícito entre as fonte de dados link.

5. Desafios e oportunidades

Quais foram os maiores desafios que você enfrentou durante o processo de mapeamento?

Falta de imagens aéreas adequadas, falta de declaração do tipo de licença dos dados nos conjuntos de dados oficiais da região, a fim de que pudessem ser importados para o OSM.

O que poderia ser melhorado nos esforços futuros de mapeamento para torná-los ainda mais eficazes na resposta a desastres?

Melhorar a comunicação com os grupos de mapeadores(as) e entidades envolvidas no desastre. Aumentar a utilização de programas e dados abertos, especialmente, aqueles que fazem parte do ecossistema do OSM, voltados à causa humanitária, como os gestores de tarefas (tasking managers), portal Humanitarian Data Exchange (HDX) e APPs de campo que suportam mapeamento off-line (e.g. KoboToolbox) ou os mapas impressos (Sketch Maps), especialmente, nas localidades com infraestrutura precária de Internet.

6. Impacto e perspectivas futuras

Houve alguma mudança nas políticas locais ou na gestão da terra como resultado dos esforços de mapeamento?

Ainda não, pois o evento é relativamente recente. Mas, o governo federal tem um programa atualmente para fomentar a criação e adoção de planos municipais de redução de riscos, não apenas para regiões afetadas por desastres hidrológicos (e.g. enchentes, inundações) e geológicos (e.g. movimentos de massa), como também para as áreas que estão sofrendo com processos avançados e acelerados de desertificação no Brasil, em partes da Região Centro-Oeste e Nordeste, fenômenos esses, menos discutidos no nosso país.

Como você vê o papel da cartografia digital e participativa na prevenção e resposta a desastres climáticos futuros?

O papel dos mapeamentos participativos (in loco) e colaborativos (remotos) é fundamental para a respostas aos desafios relacionados à crise climática, uma vez que estes eventos estão tendendo à sua intensificação e frequência. Os eventos necessitam de respostas rápidas, durante a sua ocorrência, além da integração entre governo, Academia e sociedade civil, a fim de melhorar a infraestrutura e os processos de gestão de riscos e desastres, nas fases pós-desastre. A adoção de soluções digitais de mapeamento aberto facilita a disseminação e o uso dos dados por diferentes operadores e promove ainda a inclusão digital de maior número de pessoas da população, à medida que têm acesso a plataformas de colaboração, onde podem enviar as suas informações (informações geográficas voluntárias, VGI). Porém, a participação precisa passar do seu primeiro nível (nível consultivo) no Brasil, permitindo que as comunidades das localidades afetadas realmente participem dos processos de tomada de decisão.

7. Insights específicos sobre Santa Maria, Porto Alegre e infraestrutura

Como a enchente impactou a infraestrutura urbana, o transporte e as estradas de Porto Alegre e Santa Maria?

O desastre promoveu um grande prejuízo em vias e edificações, mas, pelo mapa Web das vias interditadas e destruídas, os danos foram muito maiores em Porto Alegre do que em Santa Maria. Isso se deve ao fato de que o ponto mais agudo da ocorrência do desastre tenha sido na Região do Guaíba e do Centro Histórico de Porto Alegre (mas com alcance em muitas outras localidades adjacentes a esta região de centralização do evento). A imagem abaixo mostra o uMap, elaborado na época:

link da imagem

Como o mapeamento colaborativo ajudou a identificar estradas bloqueadas, pontes danificadas ou comunidades isoladas?

O mapeamento colaborativo pode ajudar na identificação de vias e pontes bloqueadas e de comunidades isoladas, uma vez que permite a contribuição de pessoas que residem ou que estejam temporariamente nos lugares de ocorrência dos desastres. As atuais plataformas de desenvolvimento de mapas Web e as redes sociais facilitam a interação entre os colaboradores de mapeamentos colaborativos digitais, mas enfrentamos um novo desafio, infelizmente, comum no nosso tempo, que são as notícias falsas (ou fake news). Incrivelmente, durante o desastre, houve pessoas que comunicaram sobre vias estarem liberadas, quando, na verdade, estavam fechadas, provocando grandes engarrafamentos e provocando prejuízos e lentidão nas ações de resgate. Um dos males da modernidade e que releva a necessidade de validação dos dados que chegam aos que conduzem os mapeamentos. Em uma entrada em seu diário de usuário do OSM, Fernando Trebien (aka ftrebien), https://www.openstreetmap.org/user/ftrebien/diary, mostra como realizou a verificação das informações. Inicialmente, foram utilizados relatos de veículos de imprensa e aqueles enviados por redes sociais, fazendo a checagem entre os meios. Posteriormente, também foram utilizadas imagens Sentinel-2, quando disponibilizadas, a fim de realizar as validações (verificação da veracidade da informação).

Há monitoramento ou mapeamento contínuo para avaliar os esforços de recuperação e reconstrução em Santa Maria e Porto Alegre?

Os monitoramentos no período pós-desastre são realizados no Brasil pelas secretarias de defesa civil, municipais e estaduais. Até o momento, não identificamos iniciativas acadêmicas ou de empresas especificamente para avaliação dos danos, pois este tipo de atividade requer o acesso a imagens aéreas de alta resolução e a ida a campo, para verificação do estado das edificações, por especialistas (engenheiros civis, geotécnicos, geólogos etc), o que pode ser proibitivo para algumas instituições, devido aos altos custos.

— Esta entrevista está também disponível em Inglês: https://doi.org/10.5281/zenodo.15058928.

– Read in Portuguese

An interview with a university in Italy gives details of the collaborative mapping carried out in response to the Rio Grande do Sul disaster

This interview is registered on Zenodo.org and available as PDF file. How to cite this interview:

Accessing information in moments of crisis - Interview with Dr. Raquel Dezidério Souto about the Rio Grande do Sul (Brazil)’s disaster occurred in April and May, 2024. Respondent: Raquel Dezidério Souto. Interviewer: Laura Bortoloni. Rio de Janeiro: IVIDES.org, 20 mar. 2025. DOI: https://doi.org/10.5281/zenodo.15058928. Licensed under the CC-BY-NC-ND 4.0 Ⓒ authors.

This interview is also available in Portuguese:* https://doi.org/10.5281/zenodo.15058822

1. Professional Profile

Can you tell us about your background and how you became involved in Cartography?

My first contact with cartography was during my undergraduate studies in Oceanography. Then I got my Master Science in Population Studies and Social Research (IBGE) and my PhD in Geography (UFRJ). Over time, I developed lines of research in collaborative mapping, with the support of digital cartography and Web mapping. The focus of my post-doctorate in geography has been the development of Web solutions for digital collaborative mapping. We are currently developing projects with free software or hybrid projects (mixing free and proprietary software), within the framework of the Virtual Institute for Sustainable Development - IVIDES.orgⓇ, a virtual research institute that I created in 2008. Some of these projects are being made possible by IVIDES DATA, its management company.

What drew you to humanitarian mapping and participatory mapping projects?

Back in 2019, we developed our first collaborative map during the oil disaster, which affected just over 50% of the entire Brazilian coastline (which totals around 8,500 km). Given the length of the coastline and the emergency nature of the event, we made available to the public a platform with the oiled locations and the photographs that were sent via the WhatsAppⓇ group. At the time, we didn’t yet know about OpenStreetMapⓇ and we built the Web map on Google My MapsⓇ (https://ivides.org/mapa-participativo-petroleo-2019-2020). We learned about the OpenStreetMapⓇ project in 2021, during a course on Leaflet (https://leafletjs.com/) offered by the State University of Rio de Janeiro. With it, we developed an interactive map during the COVID-19 pandemic (https://ivides.org/mapa-dinamico-de-incidencia-de-covid-19), which showed the data provided by the municipalities of the North and Northwest of Rio de Janeiro (Brazil).

And the campaign for Rio Grande do Sul 2024, which I’ll detail below. Then, with the training courses offered by UN Mappers (https://mappers.un.org/learning/), we got to know the world of humanitarian mapping with OSM. With the training, I joined the group of validating mappers from UN Mappers, which added more practical mapping experiences in various other countries. Joining YouthMappers as a chapter for the Federal University of Rio de Janeiro (Brazil), with which I am affiliated, provided (and still provides) the transfer of this knowledge to the academic public and to people outside the university, having promoted mapping training with OpenStreetMapⓇ for around 700 people over the last two years.

Have you worked on similar disaster response mapping projects before? If so, how does this experience compare?

Yes, whenever possible, in projects promoted by OSM user groups, the UN Mappers, the Brazilian YouthMappers chapters and some chapters in Africa, especially Mozambique and Angola. The last collaborative mapping project I took part in was (is being) the response to the disaster caused by cyclone Chido, in the Mayotte Island (official name: Département de Mayotte) https://www.openstreetmap.org/relation/3388394#map=5/-15.96/50.12, a French overseas territory, adjacent to the marine coast of Mozambique, in the Indian Ocean. The projects are available at https://tasks.hotosm.org/explore?text=Mayotte&omitMapResults=1. Collaborative mapping is very different when we adopt open data and platforms, such as tasking managers and mobile applications, which are open source and editable by others. Proprietary solutions limit the customization of projects (from a computational point of view) and prevent the sharing of data and information, which hampers the interoperability of data between different systems. Open data and interoperability are crucial in times of disaster, which require a rapid response from those dealing with the event, during and after its occurrence. Thus, we are currently adopting free solutions in disaster response and have trained the younger generations so that they are able to participate in existing projects and propose (and manage) new humanitarian collaborative mapping projects.

2. Mapping floods in RS

How did you get involved in the participatory mapping project for the Rio Grande do Sul flood?

I heard about the disaster from relatives living in Canoas, in the Guaíba region of Rio Grande do Sul (I live in Rio de Janeiro). My aunt and uncle lost their house and all their belongings after the water dam upstream from the river burst. Then there were comments about the disaster in the OSM RS group on Telegram, a group of OpenStreetMap users in that state, and by the Humanitarian OpenStreetMap Team (HOT), which made several projects available on the HOT Tasking Manager (HOT-TM). We were developing a training agenda for the thematic mapping with OSM, promoted by IVIDES.orgⓇ, and we created a collaborative mapping project in the tasking manager in order to map the Taquari-Antas River Basin https://tasks.hotosm.org/projects/16706, the region most affected by the tragedy. This project also served as support for the workshop on OSM mapping of waterways and related features, provided by Séverin Ménard (aka Severingeo), co-founder of the Les Libres Géographes (LLg), a french non-profit organization and, at that time, a consultant for the United Nations Global Service Center (UNGSC). The choice of the river basin as the project’s area of interest (AOI) is justified by the importance of assessing the occurrence of the event in a geographical rather than political-administrative division, due to the nature of the phenomenon (hydrological disaster).

What was the specific role of Humanitarian OpenStreetMap in this emergency?

HOT is a US non-profit organization that provides human and material resources for project management and open data, which facilitates collaborative mapping activities during and after disasters. The adoption of the tasking manager has grown over time and this solution, which is also being used in projects by other international organizations and is open source https://github.com/hotosm/tasking-manager, makes it possible to increase the speed of mapping, a fundamental aspect, since time is crucial when responding to emergencies. However, we still face difficulties in obtaining high-resolution and up-to-date aerial images; and the role of the organization and the usefulness or application of the open data generated are still unclear in Brazil, which could be resolved with greater awareness campaigns among the general population and public agents and with the continued promotion of courses and other training modalities.

Can you describe the context in which this initiative was launched and how it was coordinated with local authorities and NGOs?

The Rio Grande do Sul disaster involved mass movement events, flooding, inundations and river erosion, which occurred in May and April 2024, but with effects that continue to this day. As reported on the Wikipedia page created to document the disaster: https://pt.wikipedia.org/wiki/Enchentes_no_Rio_Grande_do_Sul_em_2024. In several cities, in the period between April 27th and May 2nd, it rained between 500 and 700 mm, corresponding to a third of the historical average rainfall for an entire year, and in many others the rainfall was between 300 and 400 mm between May 3th and 5th (…), according to data from the Hydraulic Research Institute (Instituto de Pesquisas Hidráulicas), of the Federal University of Rio Grande do Sul (UFRGS). May rains brought more than 14 trillion liters of water to the Lake Guaíba, a volume equivalent to almost half the reservoir of the Itaipu Hydroelectric Power Plant. (…) The excessive rainfall affected more than 60% of the state’s territory. (Free translation)

Initially, the projects available on the tasking manager were not coordinated with local authorities, as most of the municipalities affected by the disaster adopted proprietary mapping solutions on the emergence efforts. But some connection was realized with the Brazilian Ministry of Integration and Regional Development (MIDR), in order to know the most affected places. For example, the Porto Alegre municipality adopted the same solutions that were already used by the Civil Defense of the State of São Paulo, which are based on proprietary software. Unfortunately, these administrations do not recognize the importance of applications and open data, even though the advantages of using them are clear, as recognized in various scientific articles on successful case reports in risk management and dealing with the consequences of disasters. These free and open source softwares, combined with the use of OSM’s geospatial base, could improve the care and rescue of victims, as well as saving public resources, since there is no payment for licenses. It is worth remembering that the same Porto Alegre municipality later cooperated to assess the damage caused to buildings and roads, based on the data mapped by everyone, but this assessment did not involve the mapping community; it was carried out only between the NGO, the government and the BID.

3. Mapping Processes

How was the work organized between volunteers and professionals?

In our mapping project, all the mappers were managed through the tasking manager and social media - WhatsAppⓇ and digest emails groups. The mapping of features, with initial priority given to buildings and roads, was carried out by people of any level of knowledge, while validation was (and still is) carried out by mappers with intermediate or advanced levels (which corresponds to 250 or 500 data sets sent, respectively). This division is important to increase the quality of the mapping. Interaction in the groups is fundamental for solving doubts and problems and for exchanging data and information. To encourage participation in collaborative mapping activities (mapping marathons) and to communicate the results, we also publish short articles in the weeklyOSM https://weeklyosm.eu/, which has a worldwide distribution.

Were there any particular challenges in collecting data or engaging the community?

In Brazil, there are difficulties in acquiring aerial images with high resolution to adequately map buildings in the pre-, during- and post-disaster stages. Another obstacle has been the adoption of closed licenses (or the lack of declaration of the type of license) in the official datasets. Some municipalities have entered into agreements in which private companies provide the services and really act as the owner of the data. But, according to the Brazilian law, this data needs to be publicly available - Federal Law no 12.527/2011, https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2011/lei/l12527.htm. Part of the Brazilian population does not have adequate resources (such as good computers, smartphones and access to the high-speed networks for Internet), especially in the North and Northeast regions of Brazil, which limits their participation. The lack of training to operate the programs is also a limiting factor for mapathons (mapping marathons) and we have been working at IVIDES.orgⓇ to promote education for more mappers, in order to collaborate in the mapping projects.

4. Community Participation

What strategies did you use to make the map accessible and understandable to those affected by the flood?

We organized some public events, in order to engage people for collaborative mapping projects. One of the events mentioned above was the workshop on thematic mapping of waterways and related features. The other event was the Scientific Seminar for Rio Grande do Sul, realized in Portuguese and with a great audience https://ivides.org/seminario-rs, where researchers from the RS’ public universities (FURG, UFRGS and UERGS), who have carried out actions related to dealing with the consequences of the disaster, were able to show their mappings, exchange information and discuss the difficulties encountered. The events were held remotely in order to reach a wider audience and two videos were made available on the IVIDES.orgⓇ channel - https://www.youtube.com/live/fD5kp_j6w_Y and https://www.youtube.com/live/Dv9t2ZTRsWQ, reaching seven thousand views. The project page on our institutional portal also helped disseminate this initiative and promoted public access to the official data we were able to gather at the time from various sources (https://ivides.org/desastre-rio-grande-do-sul-brasil-2024).

Can you share an example of how participatory mapping had a direct impact on relief operations?

During this same disastrous event, the roads and bridges that were blocked or destroyed were mapped by the OSM RS community, especially by the mapper Fernando Trebien (aka ftrebien), and this data formed part of the Web map that we created with the french uMap platform (https://umap.openstreetmap.fr/pt-br/map/situacao-vias-rs_1070918). This data was used by the Autonomous Highway Department (in Portuguese, DAER-RS) to update its own map of affected roads, even though it is not explicit among the data sources link here.

5. Challenges and Opportunities

What were the biggest challenges you faced during the mapping process?

Lack of suitable aerial images, lack of declaration of the type of license of the data in the official datasets of the region, so that they could be imported into OSM.

What could be improved in future mapping efforts to make them even more effective in disaster response?

Improve communication with the groups of mappers and entities involved in the disaster. Increase the use of open programs and data, especially those that are part of the OSM ecosystem, such as tasking managers, the Humanitarian Data Exchange (HDX) portal and field APPs that support offline mapping (e.g. KoboToolbox) or printed maps (e.g. Sketch Maps), especially in locations with poor Internet infrastructure.

6. Impact and Future Perspectives

Have there been any changes in local policies or land management as a result of the mapping efforts?

Not yet, as the event is relatively recent. But the federal government currently has a program to encourage the creation and adoption of municipal risk reduction plans, not only for regions affected by hydrological disasters (e.g. floods, inundations) and geological disasters (e.g. mass movements), but also for areas that are suffering from advanced and accelerated desertification processes in Brazil, in parts of the Central-West and Northeast regions (these are less discussed in our country).

How do you see the role of digital and participatory cartography in future climate disaster prevention and response?

The role of participatory (in loco) and collaborative (remotely) mapping is fundamental in responding to the challenges related to the climate crisis, since these events are tending to increase in intensity and frequency. Events require rapid responses during their occurrence, as well as integration between government, Academia and civil society organizations in order to improve risk and disaster management infrastructure and facilitate the processes in the post-disaster phases. The adoption of open digital mapping solutions promotes the dissemination and use of open data by different actors and also increases the digital inclusion of more people, as they have access to collaboration platforms where they can send their local information (voluntary geographic information, VGI). However, participation needs to go beyond its first level (only the consultative level) in Brazil, allowing the communities of the affected localities to really participate in decision-making.

7. Specific Insights on Santa Maria, Porto Alegre, and Infrastructure

How did the flood impact the urban infrastructure, transportation and roads of Porto Alegre and Santa Maria?

The disaster caused a great deal of damage to roads and buildings, but from the map of the roads that were blocked and destroyed, the damage was much greater in Porto Alegre than in Santa Maria. This is due to the fact that the most acute point of occurrence of this tragedy was in the Guaíba Region and the Historic Centre of Porto Alegre (but with a reach into many other locations adjacent to this region where the event was centralized). The picture below shows the uMap at the time:

How did participatory mapping help in identifying blocked roads, damaged bridges, or isolated communities?

Participatory mapping can help identify blocked roads and bridges and isolated communities, as it allows people who live or are temporarily in the places where disasters occur to contribute. Current Web map development platforms and social networks facilitate interaction between collaborators in digital collaborative mapping, but we face a new challenge, unfortunately common in our time, which is fake news. Incredibly, during the disaster, there were people who reported that roads had been cleared, when in fact they were closed, causing major traffic jams and causing damage and slowing down rescue efforts. This is one of the evils of modernity and highlights the need to validate the data that reaches the mappers. In an entry in his OSM user diary, Fernando Trebien (aka ftrebien) https://www.openstreetmap.org/user/ftrebien/diary shows how he checked the information. Initially, he used reports from the Web and those sent by social networks, checking between the media. Subsequently, Sentinel-2 images were also used, when available, in order to carry out validations, checking the veracity of the information.

** Is there ongoing monitoring or mapping to assess recovery and reconstruction efforts in Santa Maria and Porto Alegre?**

Post-disaster monitoring is carried out in Brazil by the civil defense departments. To date, we have not identified any Brazilian academic or private company initiatives to monitor the damage and the reconstruction efforts, as this type of activity requires access to high-resolution aerial images and fieldwork realized by specialists, like civil engineers, geotechnicians, geologists et al., to check the condition of the buildings, which can be prohibitive for some institutions due to the high costs.

(Translated with Deepl and validated by human).

This interview is also available in Portuguese: https://doi.org/10.5281/zenodo.15058822.

Headed to Dutchess County in NY to update a smaller commercial Area. Added the parking lots, turn lanes, and buildings for the commercial areas that were missing. Still a lot of details to go.

and a detail shot

Working on this while the community inputs on the Building import project for New york.

Hopewell OSM Link

Die FOSSGIS-Konferenz 2025 findet vom 26.-29. März 2025 im Schloss Münster und Online statt. Es sind nur noch wenige Tage bis zur Konferenz. Der Countdown läuft und die Vorbereitungen laufen auf Hochtouren! Die Konferenz wird vom gemeinnützigen FOSSGIS e.V, der OpenStreetMap Community in Kooperation mit dem Institut für Geoinformatik der Universität Münster organisiert.

Auch in diesem Jahr freuen wir uns über ein großes Interesse an der Konferenz. Die Tausender-Marke wird erneut geknackt. Es werden 750 Teilnehmende vor Ort in Münster erwartet und über 250 Teilnehmende schalten sich Online dazu.

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FOSSGIS 2025 Programm

Das FOSSGIS Team freut sich auch in diesem Jahr auf ein spannendes und reichhaltiges Programm mit zahlreichen Vorträgen, ExpertInnenfragestunden, Demosessions, BoFs und Anwendertreffen und sowie 21 Workshops.

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In den Workshops sind noch Plätze frei. Buchen Sie gerne noch einen Workshop und nutzen Sie die Chance in kurzer Zeit Wissen zu einem Thema aufzubauen.

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Rund um die Konferenz gibt es zahlreiche Möglichkeiten sich zu vernetzen. Hier sind die Anwendertreffen (Onlineteilnahme ist möglich) zu nennen, aber auch die Abendveranstaltungen und der OSM Samstag und der Community Sprint am Samstag.

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Archiv FOSSGIS-Konferenzen

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Das FOSSGIS Team 2025 wünscht eine gute Anreise und freut sich auf eine spannende Konferenz in Münster

I maintain a web map style that shows walking and cycling route names. For the cycle routes, it shows the ref. For some time I’ve massaged some of the names so that e.g. National Byway loops show as “NB (loop)” just like on the signage. However, as can be seen from the example above, some hiking route names are a bit convoluted - they’re more like descriptions than names.

For example, https://www.openstreetmap.org/relation/3971851 is the England Coast Path. Open up the list of members to see the names, which includes such delights as “King Charles III England Coast Path: Southend-on-Sea to Wallasea Island”. I’m pretty sure that it doesn’t say that on the signs there.

My preference would actually be something like this, with from and to tags, and a name that matches the signs, but clearly lots of people don’t do that. There are wiki pages that support https://wiki.openstreetmap.org/wiki/Names#Name_is_the_name_only and ones that support silly names. However, this diary entry isn’t about that - I was wondering if I could actually infer sensible names in the database or .mbtiles file that I create maps from.

It turns out that relatively few relations in the area that I’m interested in have this problem - a few National Trails in England and Wales**, and a few regional routes split for other reasons. Note that something like GM Ringway Stage 8 looks like a valid name to me - although despite having walked a bit of it, I can’t remember if the signs actually have the stage number on them.

The result is this:

The shorter name means that there’s room for the name of the viewpoint now as well. The change is common to both raster and vector maps - and on vector maps the England and Wales National Trails get their own black and white shield too.

** Incidentally, I do wonder if the web designers at National Trails are closet Republicans - they’ve done literally the least possible amount of work to support the renaming of “England Coast Path” to “King Charles III England Coast Path”, not even moving it to its new alphabetical place in the list.

Import dans Openstreetmap des espaces de stationnement de vélos et trottinettes électriques en free floating de la MEL

Contexte

La MEL a depuis mars 2024 lancé un appel à projet (s’inscrivant dans l’Action 34 du Plan de mobilité horizon 2035 ), auquel Lime et auparavant Tier ont répondu pour déployer une flotte de VAE (vélo à assistance électrique ) et de TE (trottinette électrique) sur les communes qui le souhaitent. 68 communes ont répondu favorablement au déploiement du service sur leur territoire.

La détermination des espaces de stationnement se fait de la manière suivante : - Propositions d’emplacements faites aux communes par la MEL au regard de critères d’attractivité, de maillage du territoire, d’occupation de l’espace public. - Avis des communes sur les localisations. - Formalisation des emplacements exacts inscrits dans les arrêtés municipaux d’occupation temporaire du domaine public. - Travaux de marquages au sol réalisés par la MEL après réception des arrêtés municipaux et conformément à ceux-ci.

Précision de la données

Les données de ces espaces de stationnement ont été publié en Opendata en juillet 2024 :

https://data.lillemetropole.fr/geonetwork/srv/fre/catalog.search#/metadata/3b58eafd-19c5-404c-ad90-ed4035535fc7

Le point est normalement précis, il peut y avoir quelques décalages lorsque la station est masquée par un obstacle (arbres, préau) ou lorsque l’entreprise de marquage a décalé son emplacement prévu lors de l’opération de marquage.

Après vérification avec des points connus et l’ortho photo de l’IGN, les points sont assez précis ; après croisement avec les bâtiments, seuls 2 points sur 1300 ont dû être décalé de quelques mètres.

Au total, 1387 espace de stationnement ont été définis.

Les attributs sont également bien renseigné et exhaustif sur l’ensemble du jeux, le champs type_engin permet de distinguer les espaces dédiés au seul VAE et ceux dédié au TE et VAE.

Quels tags choisir ?

J’avais lancé le sujet sur le forum pour essayer de définir cet objet assez complexe au final. https://forum.openstreetmap.fr/t/parking-velo-trottinette-en-free-floating/15356/10

Finalement, après avoir fait quelques requêtes ici et ailleurs, il semble-il avoir un consensus sur le fait que l’espace soit définis comme un espace de stationnement et non de location. L’espace de stationnement passe par le tag concernant le vélo “bicycle_parking” car il n’existe pas encore de tag assez mûr pour définir un espace de stationnement trottinette.

Voici les trois cas possibles :

Un emplacement dédié uniquement aux vélos en free floating :

amenity = bicycle_parking + bicycle_parking = floor + small_electric_vehicle = yes

Un emplacement dédié à la fois aux vélos et aux trottinettes en free floating :

amenity = bicycle_parking + bicycle_parking = floor + small_electric_vehicle = yes

Pour un emplacement dédié uniquement aux trottinettes en free floating :

amenity = bicycle_parking + bicycle_parking = floor + small_electric_vehicle = only

Pour ce cas-là j’ai vu également cette possibilité, mais non répertorié dans le wiki : amenity = small_electric_vehicle_parking

Mais ce cas-là n’existe pas sur la MEL car c’est toujours une flotte de VAE + TE ou seulement VAE.

Les éléments que j’ai gardé de la base de la MEL et ceux que j’ai ajouté

Je n’ai pas gardé l’adresse et le numéro de rue, car le niveau de correction a effectué était trop important et la donnée par vraiment nécessaire.

Bicycle_parking = floor indique que c’est un stationnement au sol sans mobilier, access = customers que c’est seulement pour les clients. small_electric_vehicle = yes indique que le stationnement vélo est ouvert aux trottinettes et dans le cas de small_electric_vehicle = no on comprend que c’est seulement pour les vélos.

Préparation et import des données

La donnée a été préparé avec QGIS. J’ai importé également dans QGIS les éléments déjà présents dans OSM afin de faire un export des données MEL à importer sans ces données déjà présentes que j’ai ensuite corrigé à la main ultérieurement.

Le plugin opendata était préalablement installé. Import dans JOSM du shp des données MEL tronqué des donnée OSM déjà présente donc et correction de certain champs directement dans JOSM.

Puis import des données dans OSM : 1351 objets.

Correction des données déja présentes dans Openstreetmap

Une fois les données importées dans OSM, il en restait une vingtaine à corriger manuellement puisqu’elle était déjà présente : ajout de tous les attributs manquants : code postale, ville, code insee, id_origin etc… et mettre source = survey pour les différencier.

Perspectives de mises à jour :

Les données sont amenées à évoluer car des communes peuvent demander à arrêter le service, des emplacements mal définis au départ peuvent être supprimé, ou encore d’autres espaces peuvent se créer et d’autres communes intégrer le dispositif.

Les contributeurs les retireront petit à petit. Mais il sera toujours possible de croiser les données OSM avec une base mise à jour du coté MEL.

Résultats !

et voici une carte umap qui effectue une requête sur les espace de stationnement vélo de free-floating ainsi que les stations V’Lille

https://umap.openstreetmap.fr/fr/map/mel-emplacement-free-floating-lime_1192747

جسم

تخييم

اماكن تخييم

اماكن مراعي اغنام

There’s a saying in a certain article on the OpenStreetMap wiki that “tagging for the renderer” is equivalent to “lying to the renderer.”

Not only that, but the article also restricts the definition and meaning of “tagging for the renderer” as “the bad practice of using incorrect tags for a map feature so that they show up in the mapper’s renderer of choice. Such tagging goes against the basic good practice principles.”

I think that “tagging for the renderer” as a term should first be treated as neutral. On its own, there is no implication that “tagging for the renderer” forces us to lie to the system. Sometimes, people want to do tagging for the renderer simply because they want to place cool symbols around their area in OSM Carto.

Take me, for example.

Several months ago, I decided to download the entire openstreetmap-carto GitHub repository to analyze all of the (cool) icons contained within it and determine which tag combinations were needed to summon such icons on the OSM default map tile.

I found that the charging station icon was really cool. I loved its light blue color scheme, and its visibility on the map tile was quite good—it was already displayed at zoom level 17, on par with bank, gallery, and embassy icons.

I wanted to place this icon around my neighborhood soon. But alas, I didn’t know where any charging stations were located. So I shelved this idea for weeks and months.

Then, during a work trip to Bandung, while walking past the campus I attended as a student several years ago, I finally saw one. A charging station in the wild! It was stationed right in front of the parking area of the Labtek V building.

I was so elated—it felt like finding a legendary Pokémon in the wild! At that moment, I immediately stopped walking, opened Vespucci, and mapped the charging station.

Vespucci is nice and easy to use. Its GPS feature allows us to immediately zoom in to the current map location. Then, I simply clicked the up-and-down icon on the bottom sidebar to download the OSM data for the area, added a new node, searched for the tag preset (typing “charging” was enough to summon amenity=charging_station—no need to memorize the tag scheme at all), and it was done.

That was my first charging station mapping, purely motivated by “tagging for the renderer,” because I thought its Carto icon was really cool.

Weeks after that, I found more and more charging stations in the wild while on work trips—

• In the parking area of Metropolitan Mall Bekasi while waiting for a Biskita bus.
• In a certain building near the Ministry of State-Owned Enterprises office, while walking from Gondangdia Station to the National Library.
• In a certain building across the road near the U.S. Embassy and Consulates in Indonesia, also while walking from Gondangdia Station to the National Library.

All of them were fueled by my strong motivation to “tag for the renderer.”

It is 2025 and the whole Mapillary setup needed an upgrade. I was looking for a 360° setup and couldnt decide until i met Mr. Panoramax Christian Quest in Karlsruhe who recommended the Kandao Qoocam 3 Ultra.

Now i am playing around a bit with the Qoocam 3 and there are a multitude of ways of taking images with various intervals, settings, and post-processing pipelines.

There are issues with the Qoocam 3 Ultra though. I wish it could stitch Images in the Camera, make Interval images at 96MPixel with a frequency of less than 1Hz etc etc.

My pipeline currently uses Hugin to stitch the images. Stitching takes about a minute per Image (Hugin is sloooooow) but that can run as batch.

My pipeline currently has no "Horizon" corection which is an issue going on a 2 wheel vehicle which may lean to a side. So one would need to correct yaw, roll and pitch, depending on the orientation of the camera.

I decided to point lenses left and right because i already have 10 years worth of imagery pointing forward.

Nevertheless - here is my github repo with a shell script quickly hacked to get my workflow going.

Nevertheless - its an old wooden Tripod i still had which is rock solid. Camera is at 2,50m or something.

https://github.com/flohoff/kandao-qoocam3-ultra-stitch

UMBRAOSM Brazilian Openstreetmap Mappers Union provides several video classes on its YouTube channel. Access our content and enjoy mapping!

Video lessons for mapping objects in OpenStreetMap.

How to map buildings using the Buildings_Tools plugin in the Josm Editor

https://www.youtube.com/watch?v=nVPdf9MjvjQ

How to map a neighborhood boundary in OpenStreetMap with a custom background layer.

https://www.youtube.com/watch?v=sTe-1N2QvLY&t=2s

How to map in OpenStreetMap with the help of Mapillary images using the ID editor.

https://www.youtube.com/watch?v=dmDW5LhfQpk&t=52s

How to customize the colored painting style to offset street names in the JOSM Editor.

https://www.youtube.com/watch?v=4jOnFjtuI10&t=57s

Enabling remote control and expert mode in the JOSM editor

https://www.youtube.com/watch?v=H8qL_l18f7c

UMBRAOSM, the Union of Brazilian OpenStreetMap Mappers, has been creating video lessons for beginner mappers, and even experienced mappers can use the video lessons for mapping neighborhood boundaries, mapping buildings in JOSM, and installing plugins, as well as using Mapillary. Visit our channel on YOUTUBE. https://www.youtube.com/@umbraosm

Umbraosm, the Union of Brazilian OpenStreetMap Mappers https://www.youtube.com/@umbraosm

Access our Telegram channel https://t.me/grupoumbraosm

Con motivo del Open Data Day de este año invité a diferentes instituciones educativas de la Comarca Lagunera para recibir talleres de mapeo básico entre el 1 y el 7 de marzo.

El taller “Mapeando tu escuela” lo impartí en:

• Universidad Autónoma de la Laguna, el 5 de Marzo.
• Universidad Tecnológica de La Laguna Durango, el 7 de Marzo.

Espero más universidades se sumen a este evento el siguiente año.

06/03/2025-12/03/2025

Daily contribution activity over the past year [¹] a commit from Emin Kocan

Mapping

• RobJN has developed a proposal to deprecate socket:tesla_supercharger and socket:tesla_destination. His reasoning is as follows: It is no longer correct to have the socket:tesla_supercharger=* tag and the socket:tesla_destination=* tag. This is because:

  1. They do not represent one thing, rather they represent different things in different parts of the world.
  2. They are no longer specific to Tesla since the introduction of NACS and the move to Type2 Combo in Europe/Rest of World.

Mapping campaigns

• Canyon Runner showcased some progress in mapping Middletown Springs, Tinmouth, Henderson, and Patterson.
• In response to the recent rebranding of France’s gas transmission network from GRTgaz to NaTran, jbcharron has completed ► a migration of the French gas network data in OpenStreetMap. The month-long process resulted in the integration of 1,392 gas substations managed by NaTran, along with 41 biomethane production sites and 29 methanisation units. Additionally, the migration facilitated the cleanup of obsolete tags, including industrial=gas, pipeline=marker, and substance=gas, ensuring a more accurate representation of the network.
• Laambda19 reported the progress made in mapping the abandoned Russian town of Tenkeli.
• Ltrlg has initiated ► a project to map pavements and pedestrian crossings in Angers, France.
• The Trufi Association, HOT, and Codeando México, with funding from GIZ, are digitalising informal transport in Mexico using OSM.
• ChaireMobiliteKaligrafy, a team of civil engineers and trainees at the University of Montréal announced /
  the completion of a revision of the highway network around Montréal in OSM. This triggered a discussion about a ‘transition model’ that this organised mapping group implemented without first discussing with the local community. Highway exits were shortened with a new location for the highway junctions based on the concept of the ‘theoretical gore‘ and the addition of placement=transition segments to adapt for the routing of autonomous cars. Details of this transition model seems unusual with various transition types, including for roundabouts and service roads in parking lots (some 19,000 transition segments were added around Montréal).
• GA_Kevin organised a virtual mapping event on 1 and 2 March, 2025, to enhance the map data coverage of Port Townsend, Washington, United States. Within the first two days, participants mapped 36% of the project, with efforts still ongoing.

Community

• Bob Cameron has given us an update on his detailed hardware configurations for capturing street views from his camper’s windows using old phones and laptops, sharing his preferred mapping techniques for capturing images and uploading them to Mapillary.
• Kumakyoo has been developing a new file format for OSM for a year (design criteria: fast access, small size, simple structure). He has started a series of blog posts about this format and hopes for feedback.
• Several Colombian mappers have shared their experience of participating in Open Data Day 2025. They organised an activity called ‘Schools on the map: A path to resilience’. You can read the following posts from: Sebas ► from Pasto, Néstor ► from Villavicencio, Valentina ► from Yopal, and Santiago ► from El Carmen del Viboral.
• Rob Nickerson has proposed introducing rules for marking some mailing lists as archived. He also started a conversation on bicycle charging tagging.
• OpenHistoricalMap contributor BMACS001 shared their mapping of the evolution of municipal boundaries in the British colony and now US state of New Jersey from the 17th century to the present, illustrating the New York–New Jersey Line War and boroughitis.
• The YouthMappers UFRJ (Federal University of Rio de Janeiro, Brazil) chapter celebrated its second birthday on Friday 14 March, and their coordinator, Prof. Dr. Raquel Dezidério Souto, interviewed some distinguished people who are dedicating their lives to working on disaster risk reduction in Brazil. The interviews, 1 ► , 2 ►, 3 ►, can be found on the OSM wiki.
• Valentin Lebossé, of Actu, published ► an interview with François Lacombe, an OpenStreetMap contributor leading a community effort to map France’s electricity and telecom poles. This initiative aims to offer a data-driven perspective on the challenges surrounding the deployment of a fibre optic network.

OpenStreetMap Foundation

• The OpenStreetMap Foundation has announced that the 2025 State of the Map will take place in Manila, Philippines, 3 to 5 October. The event will once again follow a hybrid format, allowing both in-person and virtual participation. For updates on speakers, the agenda, ticketing, and participation opportunities, visit 2025.stateofthemap.org.

Local chapter news

• The OpenStreetMap Indonesia community proudly congratulated one of its members, Andi Muhlis, for winning the State of the Map 2025 logo competition. His design, inspired by Manila’s iconic jeepneys and featuring a detailed segment of the city’s map, will serve as the official logo for the global conference.

Events

• HeiGIT is participating to this year’s Humanitarian Networks and Partnership Weeks with a session on the practical applications of AI in humanitarian work, presented by Sukanya Randhawa, Lead Machine Learning for Good.

OSM research

• HeiGIT reports that Steffen Knoblauch and others have published a paper on a study that used street-level crime-safety scores and routing-based simulations with openrouteservice to examine how crime in Rio de Janeiro affects unequal access to education. openrouteservice provides a wide range of routing services based on OSM data.

Humanitarian OSM

• HOT has launched a humanitarian mapping campaign, in collaboration with the municipal government of Bahía Blanca, to support relief and reconstruction efforts. More information is available on the project’s wiki.

Maps

• Juan Carlos Torres has developed an OpenStreetMap-based interactive map that enables users to access volume functions by tree species based on location. These volume functions estimate the volume of standing trees to help determine the amount of wood in a forest, which is essential for forest management and conservation.
• Hidde has released OpenRailwayMap-Vector, a re-implementation of OpenRailwayMap utilising vector tiles. The project currently covers Europe and North America, with plans underway to expand globally with daily updates.
• Monday Merch has launched Europe Rise, an interactive map that shows the location of the headquarters of startups, scale-ups, and established companies across Europe’s tech scene.

OSM in action

• Darkonus has observed that Rheinmetall’s Skyranger 35, a mobile air defence turret system, was using the OpenStreetMap Carto-like tile style in its navigational map in an official promotional video. This discovery has raised concerns about the possible use of OpenStreetMap data in military applications.

Open Data

• Konstantin Kuznetsov has compared data from the Government Address Registry (GAR) of Russia and the Public Cadastral Map with OpenStreetMap address data. After converting OSM address data using their ‘Pullenti Address‘ ► address normalisation service, 88% of OSM buildings mapped to the GAR data. However, OSM contains only 24% of addresses from the GAR and 21% of addresses from the cadastral dataset.

Programming

• Alex Chan demonstrated how to generate static map images using OpenStreetMap and Pillow, a Python imaging library fork, emphasising the need for standalone image exports instead of building Leaflet-based interactive embedded maps.

Releases

• [1] There are several new features on the OpenStreetMap.org website:

  • Tom Hughes has merged two commits from Emin Kocan, introducing a heat map visualisation on the OSM user profile pages to showcase daily contribution activity over the past year. This pull request had been in development since December 2024, and after further discussions and code reviews, it was finally merged on 12 March.
  • Máté Gyöngyösi has added anchor link buttons () to changeset comments on OpenStreetMap. This makes it easier to reference them directly. Máté thanked Dan Jacobson for submitting the idea four years ago, as well as Andy Allan, Holger Jeromin, Anton Khorev, and Tom Hughes for their kind support through the PR review.
• Sarah Hoffmann has released updates for Waymarked Trails, a tool for browsing OSM route relations, with a key improvement enabling it to recognise member roles in route relations, including direction, roundabouts, and alternative routes. A new feature also allows users to check a route’s order state for easier maintenance.

Did you know that …

• … weeklyOSM has featured at least 109 uMap-related stories since its inception?

Other “geo” things

• The Heidelberg Institute for Geoinformation Technology is offering an International Postdoctoral Fellowship for GIScience Research. This fellowship run for 6 to 12 months and will focus on GIScience research, with a special emphasis on work related to upscaling actionable geoinformation for climate mitigation and adaptation.
• Varsity surveyed University of Cambridge students to map and analyse ‘the Cambridge bubble’, the typical area the students frequent during a term-time week.

Upcoming Events

Where	What	Online	When	Country
Marseille	Conférence présentation d’OpenStreetMap		2025-03-14
Kalyani Nagar	Mapping Marathon		2025-03-15
Marseille	Balade et cartopartie – Parcours de fraîcheur		2025-03-15
Comuna 13 – San Javier	Junta OSM Latam – Avances SotM Latam 2025 Medellín		2025-03-15
Metz	Cartopartie – Quartier du Pontiffroy Metz		2025-03-15
	Local Chapters and Communities Congress 2025		2025-03-15
Bengaluru	OSM Bengaluru Mapping Party		2025-03-16
	Panoramax monthly international meeting		2025-03-17
	Missing Maps London: (Online) Mid-Month Mapathon [eng]		2025-03-18
Lyon	Réunion du groupe local de Lyon		2025-03-18
Bonn	186. OSM-Stammtisch Bonn		2025-03-18
San Jose	South Bay Map Night	✓	2025-03-19
Lüneburg	Lüneburger Mappertreffen	✓	2025-03-18
Stainach-Pürgg	16. Österreichischer OSM-Stammtisch (online)	✓	2025-03-19
Zürich	Missing Maps Zürich Mapathon		2025-03-19
Karlsruhe	Stammtisch Karlsruhe		2025-03-19
	OSMF Engineering Working Group meeting		2025-03-21
Düsseldorf	Düsseldorfer OpenStreetMap-Treffen (online)		2025-03-21
Chemnitz	Chemnitzer Linux-Tage 2025		2025-03-22 – 2025-03-23
Stadtgebiet Bremen	Bremer Mappertreffen		2025-03-24
Derby	East Midlands pub meet-up		2025-03-25
Münster	FOSSGIS-Konferenz 2025		2025-03-26 – 2025-03-29
	[Online] OpenStreetMap Foundation board of Directors – public videomeeting		2025-03-27
Zürich	Mapathon @ UZH DSI 2025		2025-03-28
Münster	FOSSGIS 2025 – OSM-Samstag		2025-03-29
	Green Open Data Day (ou Dia Verde dos Dados Abertos)		2025-03-31 – 2025-04-01
Seropédica	Green Open Data Day 2025 (ou Dia Verde dos Dados Abertos 2025)		2025-03-31 – 2025-04-01

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 Grass-snake, HeiGIT, MarcoR, MatthiasMatthias, PierZen, Raquel Dezidério Souto, Strubbl, TheSwavu, TrickyFoxy, assanges, barefootstache, derFred.
We welcome link suggestions for the next issue via this form and look forward to your contributions.

مياه

سكن رعوي

صفيح زيقوا

En un reciente análisis publicado en mi blog, exploré la distribución de piscinas en Santa Cruz de la Sierra utilizando datos de OpenStreetMap. La hipótesis detrás del estudio es que la presencia de piscinas privadas puede ser un indicador del nivel socioeconómico de una zona, considerando los costos asociados a su construcción y mantenimiento. Puedes leer más sobre esto en el artículo completo aquí: ¿Dónde están las piscinas?

Previo al análisis, dediqué aproximadamente tres semanas a mapear alrededor de xxxx piscinas en el área metropolitana de la ciudad, utilizando JOSM para la edición de datos. Este esfuerzo fue clave para mejorar la cobertura de OSM en la región y asegurar que la base de datos reflejara con mayor precisión la distribución real de estas infraestructuras. Durante este proceso, utilicé imágenes satelitales (Imágenes aéreas de ESRI mundial).

Para el análisis espacial, empleé exclusivamente software libre. La extracción de datos se realizó con el complemento QuickOSM en QGIS, lo que permitió filtrar rápidamente las entidades etiquetadas con leisure=swimming_pool. Luego, mediante técnicas de geoprocesamiento, se analizaron patrones espaciales y su relación con la estructura urbana de la ciudad.

La visualización de los datos en mapas temáticos mostró una fuerte concentración de piscinas en ciertas zonas, alineándose con las áreas de mayor poder adquisitivo según otras fuentes de referencia. Este tipo de análisis basado en datos abiertos resalta el potencial de OSM no solo para la cartografía colaborativa, sino también como insumo para estudios de geomarketing y planificación urbana.

El uso de OpenStreetMap y herramientas de software libre como QGIS abre muchas posibilidades para la investigación aplicada. Si tienes interés en análisis similares o en mejorar la cobertura de datos en tu ciudad, contribuir a OSM con más detalles sobre equipamiento urbano es una excelente manera de fortalecer la base de datos.

Si tienes comentarios o experiencias similares usando datos de OSM para análisis espaciales, ¡me encantaría conocerlos!