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http://www.openstreetmap.org/images/osm_logo.png" alt=""/> OpenStreetMap (openstreetmap.org) is a global collaborative mapping project, which offers maps and map data released with an open license, encouraging free re-use and re-distribution. The data is created by a large community of volunteers who use a variety of simple on-the-ground surveying techniques, and wiki-syle editing tools to collaborate as they create the maps, in a process which is open to everyone. The project originated in London, and an active community of mappers and developers are based here. Mapping work in London is ongoing (and you can help!) but the coverage is already good enough for many uses.

Browse the map of London on OpenStreetMap.org

Downloads:

The whole of England updated daily:

• england.osm.bz2 ~185M - .osm formatted raw XML data (compressed)
• england.shp.zip ~156M - ESRI shapefiles

For more details of downloads available from OpenStreetMap, including downloading the whole planet, see 'planet.osm' on the wiki.

Data access APIs:

Download small areas of the map by bounding-box. For example this URL requests the data around Trafalgar Square:
http://api.openstreetmap.org/api/0.6/map?bbox=-0.13062,51.5065,-0.12557,51.50969

Data filtered by "tag". For example this URL returns all elements in London tagged shop=supermarket:
http://www.informationfreeway.org/api/0.6/*[shop=supermarket][bbox=-0.48,51.30,0.21,51.70]

The .osm format

The format of the data is a raw XML represention of all the elements making up the map. OpenStreetMap is composed of interconnected "nodes" and "ways" (and sometimes "relations") each with a set of name=value pairs called "tags". These classify and describe properties of the elements, and ultimately influence how they get drawn on the map. To understand more about tags, and different ways of working with this data format refer to the following pages on the OpenStreetMap wiki.

• .osm - About the raw XML data format of OpenStreetMap
• Downloading data - More details of the API and extract download options
• Xapi - The extended api for filtering by tag
• Map Features - A list of tags, and what map features they represent
• Converting map data between formats - catalogue of tools for converting from .osm to other formats

Simple embedded maps

Rather than working with raw map data, you may prefer to embed maps from OpenStreetMap on your website with a simple bit of javascript. You can also present overlays of other data, in a manner very similar to working with google maps. In fact you can even use the google maps API to do this. See OSM on your own website for details and links to various javascript map libraries.

Help build the map!

The OpenStreetMap project aims to attract large numbers of contributors who all chip in a little bit to help build the map. Although the map editing tools take a little while to learn, they are designed to be as simple as possible, so that everyone can get involved. This project offers an exciting means of allowing local London communities to take ownership of their part of the map.

Read about how to Get Involved and see the London page for details of OpenStreetMap community events.

This dataset was extracted from OpenStreetMap (OSM) across the geographic area of Australia on 02 December 2021. Its purpose is to display Roads and Paths as lines within Australia. Note, however, as this dataset is built by a community of mappers, there is no guarantee of its spatial or attribute accuracy. Use at your own risk. For more information about the map features represented in this dataset (including their attributes), refer to the OpenStreetMap Wiki. Please note: The original data for this dataset has been downloaded from Geofabrik on 02 December 2021. Due to changes in tagging, previous versions of OSM may not be comparable with this release.

Extraits de la base de données géographique OpenStreetMap, carte collaborative. Les données sont issus du service d'extraction de la société GeoFabrik. Plus d'infos sur la page GeoFabrik de l'extrait.

Overview: osm: Military rasterized from OSM landuse polygons, first to 10m spatial resolution and after downsampled to 30m by spatial average.

Traceability (lineage): The class-wise layers of this dataset were extracted from OpenStreetMap data downloaded from geofabrik.de and aggregated based on labels assigned to the volunteered geographical information objects.

Scientific methodology: nan

Usability: The extracted classes can be used to preprocess training data (as detailed in Witjes et al., 2022 (in review, preprint available at https://doi.org/10.21203/rs.3.rs-561383/v3 ). Users are advised to remember the potential inconsistencies in volunteered geographical information, however: Some regions of Europe have been less consistently mapped in OpenStreetMap. This may introduce bias in any subsequent modelling.

Uncertainty quantification: nan

Data validation approaches: This dataset has not been validated

Completeness: Volunteered geographical information often more complete in regions with more active contributors. It is likely that this dataset contains many omission errors in regions of Europe where OpenStreetMap is used less intensively.

Consistency: Volunteered geographical information often more complete in regions with more active contributors. It is likely that this dataset contains many omission errors in regions of Europe where OpenStreetMap is used less intensively.

Positional accuracy: The rasters have a spatial resolution of 30m

Temporal accuracy: The maps are based on an extract from 2020.

Thematic accuracy: The 30m pixels of each OSM extract map have values ranging from 0-100, indicating the density aggregated from 10m pixels where rasterized objects burned the value 100 in a 0-value raster.

This dataset contains 23 30m resolution raster data of continental Europe land use / land cover classes extracted from OpenStreetMap, as well as administrative areas, and a harmonized building dataset based on OpenStreetMap and Copernicus HRL Imperviousness data.

The land use / land cover classes are:

1. buildings.commercial
2. buildings.industrial
3. buildings.residential
4. cemetery
5. construction.site
6. dump.site (landfill)
7. farmland
8. farmyard
9. forest
10. grass
11. greenhouse
12. harbour
13. meadow
14. military
15. orchard
16. quarry
17. railway
18. reservoir
19. road
20. salt
21. vineyard

The land use / land cover data was generated by extracting OSM vector layers from https://download.geofabrik.de/). These were then transformed into a 30 m density raster for each feature type. This was done by first creating a 10 m raster where each pixel intersecting a vector feature was assigned the value 100. These pixels were then aggregated to 10 m resolution by calculating the average of every 9 adjacent pixels. This resulted in a 0—100 density layer for the three feature types. Although the digitized building data from OSM offers the highest level of detail, its coverage across Europe is inconsistent. To supplement the building density raster in regions where crowd-sourced OSM building data was unavailable, we combined it with Copernicus High Resolution Layers (HRL) (obtained from https://land.copernicus.eu/pan-european/ high-resolution-layers), filling the non-mapped areas in OSM with the Impervious Built-up 2018 pixel values, which was averaged to 30 m. The probability values produced by the averaged aggregation were integrated in such a way that values between 0—100 refer to OSM (lowest and highest probabilities equal to 0 and 100 respectively), and the values between 101—200 refer to Copernicus HRL (lowest and highest probability equal to 200 and 101 respectively). This resulted in a raster layer where values closer to 100 are more likely to be buildings than values closer to 0 and 200. Structuring the data in this way allows us to select the higher probability building pixels in both products by the single boolean expression: Pixel > 50 AND pixel <150.

This dataset is part of the OpenStreetMap+ was used to pre-process the LUCAS/CORINE land use / land cover samples (https://doi.org/10.5281/zenodo.4740691) used to train machine learning models in Witjes et al., 2022 (https://doi.org/10.21203/rs.3.rs-561383/v4)

Each layer can be viewed interactively on the Open Data Science Europe data viewer at maps.opendatascience.eu.

This dataset was extracted from OpenStreetMap (OSM) across the geographic area of Australia on 02 December 2021. Its purpose is to display Transport Infrastructure as lines within Australia. Note, however, as this dataset is built by a community of mappers, there is no guarantee of its spatial or attribute accuracy. Use at your own risk. For more information about the map features represented in this dataset (including their attributes), refer to the OpenStreetMap Wiki. Please note: The original data for this dataset has been downloaded from Geofabrik on 02 December 2021. Due to changes in tagging, previous versions of OSM may not be comparable with this release.

This dataset was extracted from OpenStreetMap (OSM) across the geographic area of Australia on 02 December 2021. Its purpose is to display all building outlines as an area (polygon) within Australia. Note, however, as this dataset is built by a community of mappers, there is no guarantee of its spatial or attribute accuracy. Use at your own risk. For more information about the map features represented in this dataset (including their attributes), refer to the OpenStreetMap Wiki. Please note: The original data for this dataset has been downloaded from Geofabrik on 02 December 2021. Due to changes in tagging, previous versions of OSM may not be comparable with this release.

This listing should be removed. It duplicates the main OpenStreetMap listing in a not particularly helpful way

This listing could be removed and replaced by various other options

The listing originally linked to an early precursor of the current "extract" services which now supersede this by provide various extracts of different city areas, in a variety of formats.

The UK extract service has now been shut down and a README refers to downloads.geofabrik.de instead (one such extract service)

Context

Recently I came to know about OpenStreetMap from a friend of mine which inspired me to do this. OpenStreetMap, inshort OSM data is a completely crowd-sourced collection of data containing keys such as buildings, amenity, leisure, tourism etc. The data set is usually used for maps. Learn more about it.

Content

The raw data were obtained from Geofabrik which had all details about entities present in India. The CSV files are generated by a script that takes data from OSM(OpenStreetMap) file in a ProtocolBuffer Binary format(pbf).

Datasets

• building.csv - Details about buildings such as node id(since the OSM file uniquely identifies each entry by a node id), building name, type, location(longitude - latitude), other tags. Learn about the standard tags for buildings in osm file - key: building
• amenity.csv - Amenity type, name, location, other tags. Learn about the standard tags for amenity - key: amenity.
• leisure.csv - Leisure type, name, location, other tags. Learn about the standard tags for leisure- key: leisure.

Acknowledgements

Raw data source - Geofabrik

This point dataset was extracted from OpenStreetMap (OSM) across the geographic area of Australia on 05 August 2020. Its purpose is to display points or nodes within Australia. Note, however, as …Show full descriptionThis point dataset was extracted from OpenStreetMap (OSM) across the geographic area of Australia on 05 August 2020. Its purpose is to display points or nodes within Australia. Note, however, as this dataset is built by a community of mappers, there is no guarantee of its spatial or attribute accuracy. Use at your own risk. For more information about the map features represented in this dataset (including their attributes), refer to the OpenStreetMap Wiki. Please note: The original data for this dataset has been downloaded from Geofabrik on 05 August 2020. Due to changes in tagging, previous versions of OSM may not be comparable with this release. Copyright attribution: OpenStreetMap, (2020): ; accessed from AURIN on 12/3/2020. Licence type: Open Data Commons Open Database License (ODbL) 1.0

This point of interests dataset was extracted from OpenStreetMap (OSM) across the geographic area of Australia on 05 August 2020. Its purpose is to display points within Australia which people may find of interest, this is not limited to major landmarks and include simple amenities. Note, however, as this dataset is built by a community of mappers, there is no guarantee of its spatial or attribute accuracy. Use at your own risk. For more information about the map features represented in this dataset (including their attributes), refer to the OpenStreetMap Wiki and the Points of Interest. Please note: The original data for this dataset has been downloaded from Geofabrik on 05 August 2020. AURIN has filtered the original data and omitted features to present the Point of Interests. Points of Interests have been defined by features which contain any tags within amenity, leisure, sport, tourism or historical. Due to changes in tagging, previous versions of OSM may not be comparable with this release.

Volledige OSM Mapnik rendering.

Dataset

This dataset was created by Omkar Salunke

Released under Apache 2.0

Contents

This road network dataset was created from data extracted from OpenStreetMap (OSM) across the geographic area of Australia on 05 August 2020. Its purpose is to represent motor-vehicle traversable …Show full descriptionThis road network dataset was created from data extracted from OpenStreetMap (OSM) across the geographic area of Australia on 05 August 2020. Its purpose is to represent motor-vehicle traversable public roads within Australia. Note, however, as the original dataset is built by a community of mappers, there is no guarantee of its spatial or attribute accuracy. Use at your own risk. This road network has been topologically corrected for the purposes of network analysis for motor vehicles. For more information about the map features represented in this dataset (including their attributes), refer to the OpenStreetMap Wiki. Please note: The original data for this dataset has been downloaded from Geofabrik on 05 August 2020. AURIN has filtered the original data and omitted features to present the topologically correct, motor-vehicle traversable road network. Due to changes in tagging, previous versions of OSM may not be comparable with this release. Roads presented in this dataset may not be traversable by pedestrians or cyclists. Copyright attribution: OpenStreetMap, (2020): ; accessed from AURIN on 12/3/2020. Licence type: Open Data Commons Open Database License (ODbL) 1.0

This data was downloaded from OpenStreetMap (OSM) roads data for Wisconsin from the OpenStreetMap's GeoFabrik website: http://www.geofabrik.de/data/download.html and reprojected to WTM 83/91. Several attributes were added to facilitate use of the OSM data in DNR basemaps. DNR has made edits to this data to correct errors where known and to hide road features within DNR Managed Lands that are not public roadways.This dataset does not contain Interstate Highway, US Highways, or State Highways.To report errors in this dataset, contact Bill Ceelen at William.Ceelen@wisconsin.gov. Additional information about OSM is available on the GeoFabrik site: http://www.geofabrik.de/geofabrik/openstreetmap.html

This dataset was extracted from OpenStreetMap (OSM) across the geographic area of Australia on 02 December 2021. Its purpose is to display natural features as lines within Australia. Note, however, as this dataset is built by a community of mappers, there is no guarantee of its spatial or attribute accuracy. Use at your own risk. For more information about the map features represented in this dataset (including their attributes), refer to the OpenStreetMap Wiki. Please note: The original data for this dataset has been downloaded from Geofabrik on 02 December 2021. Due to changes in tagging, previous versions of OSM may not be comparable with this release.

This dataset includes OpenStreetMap extracts for places in Nepal. The data is hosted on the Nepal OpenStreetMap Extracts website managed by GeoFabrik and GFDRR.

The placenames.* files include all city/hamlet/neighborhood/village/etc features in the OpenStreetMap database. The cities, hamlets, and neighborhood files include subsets of the placename dataset, for:

• cities.* matches tags: place=city,
• hamlets.* matches tags: place=hamlet,
• neighborhoods.* matches tags: place=neighborhood, place=neighbourhood

The data is updated every 30 minutes.

Other OpenStreetMap extracts

OSM layers / 30 minutes updates from nepal.piensa.co

Hosted on the Nepal OpenStreetMap Extracts website.

Roads

Nepal OpenStreetMap Extracts, Roads dataset on HDX.

OpenStreetMap Daily / 30 minutes updates

Various formats including for small devices (GPS, phones, tablets) on 2015 Nepal Earthquake

See the 2015 Nepal Earthquake OSM wiki page for more information on the Humanitarian OpenStreetMap Team (or HOT) response.

This dataset was extracted from OpenStreetMap (OSM) across the geographic area of Australia on 02 December 2021. Its purpose is to display Waterways as lines within Australia. Note, however, as this dataset is built by a community of mappers, there is no guarantee of its spatial or attribute accuracy. Use at your own risk. For more information about the map features represented in this dataset (including their attributes), refer to the OpenStreetMap Wiki. Please note: The original data for this dataset has been downloaded from Geofabrik on 02 December 2021. Due to changes in tagging, previous versions of OSM may not be comparable with this release.

Last update: October 16, 2025 OverviewThis point data was generated and filtered from OpenStreetMap and is intended to represent places of interest in the state of Utah. These may include businesses, restaurants, places of worship, airports, parks, schools, event centers, apartment complexes, hotels, car dealerships…almost anything that you can find in OpenStreetMap (OSM). There are over 23,000 features in the original dataset (March 2022) and users can directly contribute to it through openstreetmap.org. This data is updated approximately once every month and will likely continue to grow over time with user activity. Data SourcesThe original bulk set of OSM data for the state of Utah is downloaded from Geofabrik: https://download.geofabrik.de/north-america/us/utah-latest-free.shp.zipAdditional attributes for the Utah features are gathered via the Overpass API using the following query: https://overpass-turbo.eu/s/1geRData Creation ProcessThe Open Source Places layer is created by a Python script that pulls statewide OSM data from a nightly archive provided by Geofabrik (https://www.geofabrik.de/data/download.html). The archive data contains nearly 20 shapefiles, some that are relevant to this dataset and some that aren't. The Open Source Places layer is built by filtering the polygon and point data in those shapefiles down to a single point feature class with specific categories and attributes that UGRC determines would be of widest interest. The polygon features (buildings, areas, complexes, etc.) are converted to points using an internal centroid. Spatial filtering is done as the data from multiple shapefiles is combined into a single layer to minimize the occurrence of duplicate features. (For example, a restaurant can be represented in OSM as both a point of interest and as a building polygon. The spatial filtering helps reduce the chances that both of these features are present in the final dataset.) Additional de-duplication is performed by using the 'block_id' field as a spatial index, to ensure that no two features of the same name exist within a census block. Then, additional fields are created and assigned from UGRC's SGID data (county, city, zip, nearby address, etc.) via point-in-polygon and near analyses. A numeric check is done on the 'name' field to remove features where the name is less than 3 characters long or more than 50% numeric characters. This eliminates several features derived from the buildings layer where the 'name' is simply an apartment complex building number (ex: 3A) or house number (ex: 1612). Finally, additional attributes (osm_addr, opening_hours, phone, website, cuisine, etc.) are pulled from the Overpass API (https://wiki.openstreetmap.org/wiki/Overpass_API) and joined to the filtered data using the 'osm_id' field as the join key. Field Descriptionsaddr_dist - the distance (m) to the nearest UGRC address point within 25 mosm_id - the feature ID in the OSM databasecategory - the feature's data class based on the 4-digit code and tags in the OSM databasename - the name of the feature in the OSM databasecounty - the county the feature is located in (assigned from UGRC's county boundaries)city - the city the feature is located in (assigned from UGRC's municipal boundaries)zip - the zip code of the feature (assigned from UGRC's approximation of zip code boundaries)block_id - the census block the feature is located in (assigned from UGRC's census block boundaries)ugrc_addr - the nearest address (within 25 m) from the UGRC address point databasedisclaimer - a note from UGRC about the ugrc_near_addr fieldlon - the approximate longitude of the feature, calculated in WGS84 EPSG:4326lat - the approximate latitude of the feature, calculated in WGS84 EPSG:4326amenity - the amenity available at the feature (if applicable), often similar to the categorycuisine - the type of food available (if applicable), multiple types are separated by semicolons (;)tourism - the type of tourist location, if applicable (zoo, viewpoint, hotel, attraction, etc.)shop - the type of shop, if applicablewebsite - the feature's website in the OSM database, if availablephone - the feature's phone number(s) in the OSM database, if availableopen_hours - the feature's operating hours in the OSM database, if availableosm_addr - the feature's address in the OSM database, if availableMore information can be found on the UGRC data page for this layer:https://gis.utah.gov/data/society/open-source-places/