https://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.

CANUE staff developed the Green Roads data set by combining street network files from Open Street Map 9OSM) (downloaded Nov 29, 2020) and annual average normalized difference vegetation index (NDVI) data from LandSat 8 circa 2016 from Google Earth Engine. OSM roads categorized as primary, secondary, tertiary, tertiary link, residential, unclassified and unknown were extracted from OSM, combined into a single file and clipped to urban areas. Urban areas were defined as all dissemination blocks classified as small population centres (population 1,000 to 29,999), medium population centres (population 30,000 to 99,999) or large population centres (population 100,000 or greater) in the 2016 Census. The urban roads layer was used to extract all LandSat 8 pixels with NDVI data (30m resolution). All extracted pixels with an NDVI value of 0.3 or greater, indicating green vegetation, were converted into points. Finally, the total number or points and the average NDVI value was calculated within buffers of 250m, 500m, 750m and 1000m of DMTI single-link postal codes from 2016.

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

Last update: August 20, 2024OverviewThis 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/

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 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.

This data set is composed by 4 geojson files, that can be used to generate maps of mainland France :

• motifs_all.geojson : pattern about transport extracted from contributions of the French Great National Debate (Grand Débat National). Original dataset : https://granddebat.fr/pages/donnees-ouvertes
• bikeway_fr.geojson and railroad_fr.geojson : cycleways and railways of mainland France, from Open Street Map. Original dataset : https://download.geofabrik.de/europe/france.html
• trainstations.geojson : train stations and halts of mainland France, from Open Street Map. Original dataset : https://download.geofabrik.de/europe/france.html
• aires_urbaines.geojson : urban areas of mainland France. Original dataset : https://www.insee.fr/fr/information/2115011
• communesimportantes.geojson : the main cities of mainland France

The data set is in French.

Ce jeu de données est composé de 4 fichiers geojson qui peuvent être utilisés pour générer des cartes en France métropolitaine :

• motifs_all.geojson : motifs à propos du transport extraient des contributions en ligne au Grand Débat National. Jeu de données d'origine : https://granddebat.fr/pages/donnees-ouvertes
• bikeway_fr.geojson and railroad_fr.geojson : pistes cyclables et voies ferrées en France métropolitaine, venant d'Open Street Map. Jeu de données d'origine : https://download.geofabrik.de/europe/france.html
• trainstations.geojson : gares et petites gares en France métropolitaine, from Open Street Map. Original dataset : https://download.geofabrik.de/europe/france.html
• aires_urbaines.geojson : aires urbaines en France métropolitaine, définies par l'INSEE. Jeu de données d'origine : https://www.insee.fr/fr/information/2115011
• communesimportantes.geojson : principales villes de France métropolitaine

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.

This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps). The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin Seilaplan for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting. The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019.

Additional notes for vector data: Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API - https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any perimeter or area can be digitised freely using the QGIS editing toolbar.

The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are:

• Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM)
• Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif

This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed.

Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range.

This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.

This line (polyline) dataset was extracted from OpenStreetMap across the geographic area of Australia on 7 April 2017. It has been pruned to include only those features that have been deemed to be publicly traversable (e.g., streets and footpaths). 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. Please contact AURIN if you would like to obtain a list of features that have been pruned from the original dataset that were deemed not traversable. For more information about the map features represented in this dataset (including their attributes), refer to the OpenStreetMap Wiki. You are free to use this dataset for any purpose as long as you credit OpenStreetMap and its contributors. OpenStreetMap contributors maintain data about roads, trails, railway stations, and much more. Emphasising local knowledge, OpenStreetMap's community is diverse, passionate, and growing every day. Their contributors include enthusiast mappers, GIS professionals, engineers running the OSM servers, and more. If you find any errors/omissions in this dataset, please update OpenStreetMap to ensure it can be communicated to the broader community. This dataset was downloaded from Geofabrik on 7 April 2017.

This dataset contains the latest Open Street Map (OSM) objects for Greece, including the following elements: 1) Buildings, 2) Land Use Information, 3) Natural Objects, 4) Places, 5) Places of Faith and Worship, 6) Points of Interest, 7) Railway Networks, 8) Road Networks, 9) Points of Traffic Interest, 10) Mixed Transportation Hubs, 11) Water Bodies, and 12) Waterways.

Information and data were collected from: www.geofabrik.de

Το εν λόγω σύνολο δεδομένων περιλαμβάνει τα νεότερα Open Street Map (OSM) αντικείμενα για την Ελλάδα. Περιλαμβάνει τα ακόλουθα αντικείμενα: 1) Κτίρια, 2) Χρήσεις Γης, 3) Φυσικά Αντικείμενα, 4) Τοποθεσίες, 5) Χώροι Λατρείας, 6) Σημεία Ενδιαφέροντος, 7) Σιδηροδρομικά Δίκτυα, 8) Οδικά Δίκτυα, 9) Σημεία Συγκοινωνιακού Ενδιαφέροντος, 10) Συγκοινωνιακοί Κόμβοι, 11) Υδάτινοι Φορείς και 12) Υδάτινοι Δίαυλοι.

Οι επιμέρους πληροφορίες και δεδομένα συλλέχθηκαν από το: www.geofabrik.de

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 service allows direct geocoding (conversion of a postal address or place name into geographical coordinates) and reverse geocoding (conversion of geographical coordinates into postal address or place name). It is powered by the free Photon tool (see https://github.com/komoot/photon), powered by OpenStreetMap data relating to the former Rhône-Alpes region (see https://download.geofabrik.de/europe/france/rhone-alpes.html). The official documentation of Photon's search API is provided on GitHub, https://github.com/komoot/photon#search-api. The link to perform a query is as follows (replace the .. by the place to geocode): https://download.data.grandlyon.com/geocoding/photon/api?q=... Examples: https://download.data.grandlyon.com/geocoding/photon/api?q=lyon https://download.data.grandlyon.com/geocoding/photon/api?q=%22Rue%20garibaldi%22

This dataset is a comprehensive inventory of Alaskan buildings, storage tanks, and roads that were: (1) detected from 0.5 meter resolution satellite imagery of communities (acquired between 2018-2023) and (2) supplemented by OpenStreetMap data. We created HABITAT (High-resolution Arctic Built Infrastructure and Terrain Analysis Tool), a deep learning-based, high-performance computing-enabled mapping pipeline to automatically detect buildings and roads from high-resolution Maxar satellite imagery across the Arctic region. Shapefiles beginning with "HABITAT_AK" contain only the post-processed deep learning predictions. Shapefiles beginning with "HABITAT_OSM" contain the post-processed deep learning predictions supplemented by OpenStreetMap data. The HABITAT pipeline is based on a ResNet50-UNet++ semantic segmentation architecture trained on a training dataset comprised of building and road footprint polygons manually digitized from Maxar satellite imagery across the circumpolar Arctic (including Alaska, Russia, and Canada). The code is made available at https://github.com/PermafrostDiscoveryGateway/HABITAT. From imagery of 285 Alaskan communities acquired between 2018-2023, we detected approximately 250,000 buildings and storage tanks (comprising a 41.76 million square meter footprint) and 15 million meters of road. Building (including storage tanks) footprint polygons and road centerlines were strictly mapped within the boundaries of Alaskan communities (both incorporated places and census designated places). After the deep learning model detected building and road footprints, post-processing was performed to smooth out building footprints, extract centerlines from road footprints, and remove falsely-detected infrastructure. In particular, a buffer is created around developed land cover identified by the 2016 Alaska National Land Cover Database map, and model predictions that fall outside of the buffer are assumed to be confused with non-infrastructure land cover. Finally, we selected buildings and roads from the OpenStreetMap Alaska dataset (downloaded in June 2024 from https://download.geofabrik.de/) that do not intersect with any deep learning predictions to generate a merged OSM and HABITAT infrastructure dataset. This merged product comprises a total building footprint of 53 million square meters and a road network of 63,744 km across the state of Alaska.

Haiti Waters - Rivers

Rivers/Streams of Haiti. The first dataset produced by the CNIGS is the official one and was made available in 2008. The second river datasets cover Haiti and the Dominican Republic. Lines of river were digitized in 2011 from High resolution satellite Imagery by Open Street Map.

This dataset was extracted from OpenStreetMap (OSM) across the geographic area of Australia on 02 December 2021. Its purpose is to display Bodies of Water 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.

OSM data (WMS) on the following topics: Pharmacies, borrow instead of buy, bookcases, libraries, container locations, fire brigade, police, farm shops, parking ticket machines, second hand, repair yourself, playgrounds, gas stations, on the way to replenish drinking water, unpackaged shopping, recycling yards, eco-model regions

Source OSM data: https://download.geofabrik.de/europe/germany/nordrhein-westfalen.html, Area extension: NRW, © OpenStreetMap contributors (see https://www.openstreetmap.org/copyright)

Olyan helyszínek, ahol csomagolatlan vásárlásokat lehet végezni NRW-ben az OpenStreetMap alapján.

Minden objektum reusable_packaging:accept=yes, reusable_packaging:accept=only, reusable_packaging:offer=yes, reusable_packaging:offer=only, zero_waste=yes vagy zero_waste=only beállítással van kiválasztva.

A szolgáltatás minden este frissül. OSM adatok forrása: https://download.geofabrik.de/europe/germany/nordrhein-westfalen.html

République démocratique du Congo (RDC), Établissements éducatifs

Points + Polygons : Interactive Online Map and Tabular spreadsheet (For multipolygons lat,lon=centroid)

• First Column Thematic describes line Feature content : school, college, university
• Objets Institutions de la santé (écoles, universités, collèges)

Columns : thematic, osmtyhpe, lat (latitude), lon (longitude), osm_id (unique point id), amenity, school:FR, name, name:fr, name:en, operator:type, addr:housenumber, addr:street, addr:city, phone, building, source, source:data, source:date, source:name, source_ref, survey:date, note, attribution

• OpenStreetMap data extraction from
• ETL2HDX Procedures

OSM data as an OGC API - features on the following topics: Pharmacies, borrow instead of buy, bookcases, libraries, container locations, fire brigade, police, farm shops, parking ticket machines, second hand, repair yourself, playgrounds, gas stations, on the way to replenish drinking water, unpackaged shopping, recycling yards, eco-model regions Source OSM data: https://download.geofabrik.de/europe/germany/nordrhein-westfalen.html, Area extension: NRW, © OpenStreetMap contributors (see https://www.openstreetmap.org/copyright)

This dataset was extracted from OpenStreetMap (OSM) across the geographic area of Australia on 02 December 2021. Its purpose is to display Places of Worship 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.