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Data import process into OpenStreetMap

Posted by DannyAiquipa on 16 October 2018 in English (English)

One of the contributions could be used to improve the quality of OpenStreetMap is by importing existing datasets. However, if you don’t pay close attention, you may end up doing bad imports. Here, we collected some suggestions to guide you to import existing dataset into OSM correctly.

1. Type of data import

According to documentation in OpenStreetMap, only these physical features can be imported into OSM. They are:

  • Point: locations that can be presented by a single dot on the map, for instance, bank, restaurant or post office, etc.
  • Lines: the feature can be presented as lines, for instance, roads, trails, bike lanes.
  • Polygons: can be defined as a closed line, for instance, buildings, farmlands.

In the following picture, you can see how all the physical features that show and can be rendered on a digital map.


2. Data format for OSM

The default dataset extension of OSM is “.osm”. OSM data files are traditionally distributed in an XML format, and indeed it is still structured very much like XML. You can see an example as the following figure.



Converting other dataset to OSM In Java OpenStreetMap editor (JOSM), you can only edit feature data in ‘.osm’ format. If your data is packed in other data formats, we list all the possible conversion tools in the following table that you can use to convert your dataset into ‘.osm’.


3. OSM data license and permission

Once the dataset is in the correct format for OSM, you may consider OSM data license or permission. There are two kinds of scenarios:

  • Scenario 1. You own the data you are going to import into OSM, so you should consider an open source or public domain license. The public domain means it will be free and open to being used by anyone for any purpose without restriction under any copyright law.
  • Scenario 2. If the dataset originally owned by a third party, you need to have the permission to import the data into OSM. To obtain a permit, you can find some letter templates here. For specific details of the permission of data ownership, you can go and view it on Open Data Commons Open Database License(ODbL). If the data owner request, it needs to be addressed in the contributions specifically.

4. OSM data importation plan

You need to create a plan for any OSM data importation you are going to take, and the plan can be outlined as:


For specific you can see the following examples:

Register import plan. Edit and add your import plan in wiki OpenStreetMap Page: OSM Catalogue imports.

5. Data importation and communication with OSM community

Someone else may be working on importing the same dataset you have. Your dataset may be relevant to a region but not to other parts of the world. Therefore, keeping the OSM community informed of your intention is important. Before you import your dataset into OSM, please:

  • Send the import plan to country mailing lists.
  • Subscribe to import mailing lists to hear what the community has to say.
  • Send messages to local OSM users. You can find local OSM mapper groups by:

    • User groups: shows a map of the user groups by their geolocations;
    • Who’s around me? will show users who are close to your geolocations.
  • Also, send an email to image

  • Wait for approval of the import plan, if the local community has been approved your import plan, go ahead with the import process.

6. Other recommendations we have for you

  • Update your mapping project constantly.
  • Try to accomplish the schedule of your import plan.
  • When you are importing dataset into OSM, we recommend you to start with a smaller dataset. You should split a large area into smaller areas too.
  • Using high-resolution imagery, e.g. ESRI World Imagery, DigitalGlobe Standard Imagery, DigitalGlobe Premium Imagery, or Bing Aerial Imagery, to evaluate your dataset are recommended, but be minded that the imagery can be out of date.
  • Ask local mappers to validate and update your data that imported into OSM.


Let me know if you have any questions.

Commercial farms in Ethiopia

Posted by DannyAiquipa on 18 September 2018 in English (English)

Ethiopia’s main economic activity is Agriculture. Smallholders in Ethiopia farm around 37% of the lands and the rest 8% goes to the large-scale commercial farms. Large-scale commercial farms produce crops such as rice, maize, coffee, tea, cotton, pulse, rubber, and palm oil.

Invest in agriculture is the most important and most effective strategy for poverty reduction in rural areas. As part of Ethiopia’s development strategy, the Ethiopia government has set out to attract more foreign investment in large-scale commercial agriculture as outlined in this policy. image

Mapping Commercial farms with the World Bank

In the large commercial farm mapping task with the World Bank, we mapped more than 190 large commercial farms in lowlands Ethiopia. Ethiopia lowlands, usually defined as places below 1,500 meters in altitude, account for approximately 60 percent of Ethiopian territory and 12 percent of the population. image

Mapping process:

Big farmlands can be visible from high-resolution satellite imagery. But such satellite imagery in OpenStreetMap for Ethiopia is outdated and high cloud cover. These factors make mapping difficult.

To better locate large commercial farmlands, we used multiple satellite imagery and tools: High-resolution Satellite imagery (e.g. ESRI base map), Sentinel-2 Agriculture mosaic, and Java OpenStreetMap editor (JOSM).

  • Satellite Imagery ESRI World Imagery was used for mapping because it is up to date than others in Ethiopia lowlands image

  • Sentinel-2 Sentinel-2 is open source imagery provided by [ESA] (, it’s in 10m resolution with multi- spectral bands. It used to perform terrestrial observations like forest monitoring or land cover changes detection. The imagery is updated every 16 days. To make agriculture band mosaic, band 11, 8 and 2 combination specifically to spot farmlands. image

  • Tool for mapping JOSM is a desktop application that allows mappers to trace, map and manipulate data easily, thanks to its plugins and stability. For mapping were used ESRI and Sentinel-2 images both as base layers in JOSM. After 2 weeks of mapping, 194 commercial farms were added to OpenStreetMap. Before and after mapping: Previously, 55 farms were mapped in OpenStreetMap within Ethiopia lowlands. After, we mapped 194 farms in Ethiopia lowlands. Before and after mapping: Previously, 55 farms were mapped in OpenStreetMap within Ethiopia lowlands. After, we mapped 194 farms in Ethiopia lowlands.

In the following table, you can see the number of farms added in Ethiopia’s lowlands according to size farm in hectares. image We are looking for some validation, and Let me know if you have any questions and feedback!

Over 75, 000 schools imported in Peru!

Posted by DannyAiquipa on 6 June 2018 in English (English)

In last November 2017 there were around 700 educational institutions in OpenStreetMap, most of them in urban areas and nothing in rural areas, the information about educational institutions present in OpenStreetMap was incomplete and we really couldn’t know how many educational institutions exists in a place, or which places don’t have any schools.

Bearing in mind that many educational institutions do not exist in the map and with the purpose of having the complete information of all educational institutions of Peru in OpenStreetMap, knowing that this information will allow the government to make better decisions such as: Improve access to education, better educational politics as well as services and the quality of education in the country.

As part of a collective effort we made a coordination with the Ministry of Education, to be able to use the data of educational institutions that they manage through SIGMED’s portal and can use all data of schools and kindergartens for import into OpenStreetMap.

The import process was carried out following the import guidelines set in OpenStreetMap.

Process of School’s import

Step 1: Prerequisites

Before to start the import process of import, we identified the categories of the database of SIGMED, these categories includes categories such as Basic regular education(EBR), Basic alternative education(EBA), and productive technical education(ETP), for this process we just selected Basic Regular Education, this category includes 3 levels of education:

  • Initial
  • Primary
  • Secondary

Step 2: Community Buy-in

We talked about the import schools, in the last SOTM, this event was made in last November in Lima.

Step 3: License approval

For obtain permission to use the SIGMED data in OpenStreetMap, we made a coordination with the territorial analysis specialist of the Ministry of Education, Sergio Miness, who agreed, and also mentioned that there were plans for import but those did not come true The data of the educational institutions are available for use from the SIGMED portal, which can be used for any purpose.

Step 4: Documentation

Step 5: Import Review

After the data was downloaded of SIGMED, we selected which tags should be included, after the discussion, we considered the next tags for import:

It was also selected that OpenStreetMap tags should be added, for the correct labeling of educational institutions:

To better manage the large amount of data downloaded from the SIGMED and facilitate the import process, were created:

  • A task manager

This which allowed to work in order wayin the importation process of educational institutions, an orderly manner in the importation process of educational institutions because the process import occurs within of specific area, and the task has 4 indicators of status of this block: Not done, in progress, done and validated, in this way we can tracking the progress of task This task is available from here :

OSM tasking Manager for import Schools

  • Grid

Due to the large amount of existing data of Educational Institutions, it was decided to divide the area of the country, this was done according to the density of educational institutions by area.


Step 6: Uploading

For uploading the data into OpenStreetMap, we used an editor for OpenStreetMap - JOSM, this tool allows to view and manipulate the data prior to upload. Process of importing schools - JOSM

Finally, after 8 weeks of hard work, we managed to import more than 75, 000 educational institutions throughout Peru, we are really happy to contribute to improve the data in OpenStreetMap! This data will be available for anyone to access at the information. This is especially important for rural areas, since schools are now in OpenStreetMap, and local or regional authorities can use the data to assess the impact of the infrastructure and road networks that lead to these educational institutions, as well as to plan impacts that could occur if disasters occur, as well as allocate funds according to the need of each educational institution.

Spanish version

# ¡Más de 75, 000 escuelas fueron importadas en el Perú!

Hasta el mes de noviembre del 2017, en OpenStreetMap habían alrededor de 700 instituciones educativas en Perú, la mayoría de éstas presentes en áreas urbanas y casi ninguna en el área rural, la cantidad de instituciones educativas que se mostraba en el mapa era incompleta, y no se sabía realmente cuántas instituciones educativas existían en una ciudad o en qué ciudades no habían instituciones educativas.

Teniendo en cuenta que muchas instituciones educativas no existen en el mapa y con la finalidad de tener la información completa de todas las instituciones educativas del Perú en OSM, sabiendo que esta información permitirá al gobierno tomar mejores decisiones como: Mejorar el acceso a la educación, mejores políticas educativas así como los servicios y la calidad de la educación del país.

Como parte de este esfuerzo colectivo hicimos coordinaciones con el Ministerio de Educación del Perú, para poder usar los datos de las instituciones educativas que ellos manejan a través del Mapa de escuelas - SIGMED y poder importarlos a OpenStreetMap.

Para realizar este proceso, seguimos las directrices de importación de OpenStreetMap.

Proceso de importación de instituciones educativas

Paso 1: Pre requisitos

Antes de iniciar el proceso de importación, se identificaron las categorías de la base de datos del aplicativo Mapa de Escuelas del Ministerio de Educación del Perú (SIGMED), estas categorías incluyen: Educación Básica Regular(EBR), Educación Básica Alternativa (EBA) y Educación Técnico Productiva (ETP). Para el proceso de importación se consideró la categoría de Educación Básica Regular, esta categoría incluye 3 niveles de educación:

  • Inicial, comprende de 1 a 5 años.
  • Primaria, comprende de 6 a 11 años.
  • Secundaria, comprende de 12 a 16 años.

Paso 2: Coordinaciones con la comunidad

Se dió a conocer sobre el proceso de importación en el Estado de mapa de Latinoamérica, el que se llevó a cabo en la ciudad de Lima en el mes de Noviembre del 2017.

Paso 3: Aprobación de la licencia

Para obtener el permiso de uso de los datos de SIGMED en OpenStreetMap, se hicieron coordinaciones con el especialista de análisis territorial del Ministerio de Educación, Sergio Miness, quien estuvo de acuerdo, y también mencionó que hace un tiempo ya hubo planes de importación, pero que estos no se llegaron a concretar. Los datos de las instituciones educativas están disponibles para su uso desde el portal del SIGMED, los mismos que pueden ser usados para cualquier propósito.

Paso 4: Documentación

Se escribió una guía con todo lo relacionado al proceso de importación de las instituciones educativas, la cual está disponible aquí.

También se creó un repositorio en github para seguir el proceso de importación de instituciones educativas.

Paso 5: Revisión de la importación

Después que los datos fueron descargados de SIGMED, se seleccionó las etiquetas que deberían ser agregadas y omitidas en el proceso de importación, después se obtuvo el siguiente cuadro: cuadro1

También se seleccionaron las etiquetas de OpenStreetMap que debían ser añadidas, para el correcto etiquetado de instituciones educativas:


Para manejar mejor la gran cantidad de datos descargados del SIGMED y facilitar el proceso de importación, se crearon:

  • Un administrador de tareas

El cual permitió trabajar de manera ordenada en el proceso de importación de instituciones educativas, ya que se trabaja dentro de un área específica, y el cual tiene un indicador de estado de la tarea: No hecho, en progreso, completo y validado; el cual fue diseñado para el control y seguimiento del progreso de la importación de instituciones educativas. El link del administrador de tareas se puede encontrar aquí.

OSM tasking Manager for import Schools

  • Grilla

Debido a la gran cantidad de datos existentes de instituciones educativas, se optó por dividir el área del país, esto se realizó de acuerdo a la densidad de instituciones educativas por zona. Ejemplo:


Paso 6 Subida de datos

Para subir los datos a OpenStreetMap, se usó JOSM, el cual es un editor de OpenStreetMap, el mismo que permite la manipulación de datos, antes de subirlos a OpenStreetMap. En este ejemplo podemos observar cómo se realizó el proceso de importación de instituciones educativas. 👇

Process of importing schools - JOSM

Finalmente, después de 8 semanas de arduo trabajo, se lograron importar más de 75, 000 instituciones educativas en todo el Perú, nos sentimos realmente felices por contribuir a mejorar los datos en OpenStreetMap, ya que estos datos estarán disponibles para que cualquier persona pueda acceder a esta información. Además, esto es especialmente importante para las áreas rurales, ya que las instituciones educativas rurales, están agregadas ahora en OpenStreetMap, y las autoridades locales o regionales pueden usar los datos para evaluar el impacto de la infraestructura y las redes viales que conducen a estas instituciones educativas, también permitirán planificar los impactos que se podrían dar si ocurren desastres, así como asignar fondos de acuerdo a la necesidad de cada institución educativa.

Supporting the mapping response in Ecuador

Posted by DannyAiquipa on 5 May 2016 in Spanish (Español)

In response to the 7.8 Eartquake in the province of Manabi on April 16, the data team of Mapbox decided to help mapping the zone with the objective of improving the map of the affected zone.

ecuador ######

During the mapping time we were able to trace more than 16,000 buildings and 450 streets.

progree ###### Top editors from our data team

This was an awesome progress!

In this way, we are helping rescue teams and statistics collection become more efficient.

At the present time, the task has been completed in 100%.