GEOFABRIK download of OpenStreetMap data of Italy's center

GEOFABRIK download of OpenStreetMap data of Berlin (Germany)

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.

GEOFABRIK download of OpenStreetMap data of Ile-de-France (France)

This layer shows the roads network in Turkey from OpenstreetMap (http://download.geofabrik.de/europe.html) Refer to the Features section in the metadata for copyright information.

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 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 contains only Interstate Highway, US Highways, and 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

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 layer shows the roads network in Iran from OpenstreetMap (http://download.geofabrik.de/asia.html) Refer to Features section in the metadata for copyright information.

Geofabrik. (2024). Download OpenStreetMap data for this region. https://download.geofabrik.de/north-america/canada.htmlData was clipped to PEC boundary.

This layer represents different cultural places in Caucasus as of 2021. The data comes from OpenStreetMap and the legend comes from categories used in OpenStreetMap, https://download.geofabrik.de/asia/armenia.html. Refer to the Features section in the metadata for copyright information.

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)

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:

buildings.commercial

buildings.industrial

buildings.residential

cemetery

construction.site

dump.site (landfill)

farmland

farmyard

forest

grass

greenhouse

harbour

meadow

military

orchard

quarry

railway

reservoir

road

salt

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.

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/

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 data set can be used to reproduce the analyses made by the authors in their paper “Morphological delineation of cities based on scaling properties of urban patterns: a comparison of three methods”. It contains 12 shapefiles that represent theoretical urban patterns and 4 shapefiles that can be used to delineate the morphological agglomeration of Brussels (Belgium). It also contains a R script to calculate the carrying capacity of a logistic percolation function.Description of each file2_Figure_1: theoretical street network for testing the Natural Cities method3_Figure_2: theoretical street network for the comparison of two variants of the Natural Cities method4_Figure_3: theoretical street network to evaluate the effects of the spatial extent of the study area on the delineation of Natural Cities5_Figure_5a: theoretical pattern for testing MorphoLim (building footprints) – dense urban core6_Figure_5b: theoretical pattern for testing MorphoLim (building footprints) – less dense urban core7_Figure_6: theoretical pattern (building footprints) to evaluate the effects of the geographic extent of the study area on the delineation with MorphoLim8_Percolation_C_Calculation: R code to calculate the carrying capacity of a logistic function (Hierarchical Percolation)9_Figure_7: theoretical street network for testing Hierarchical Percolation10_Figure_8: theoretical polycentric street network for testing Hierarchical Percolation 11_Figure_9ac: theoretical urban pattern crossed by a large non built area (road intersections)12_Figure_9b: theoretical urban pattern crossed by a large non built area (building footprints)13_Figure_10ac: theoretical pattern where a built ribbon links two urban centres (roads intersections )14_Figure_10b: theoretical pattern where a built ribbon links two urban centres (building footprints)15_Belgium_buildings: cadastral data of buildings (2D) for Belgium (© 2009 Administration Générale de la Documentation Patrimoniale)16_Brabant_buildings: cadastral data of buildings (2D) for the province of Brabant (© 2009 Administration Générale de la Documentation Patrimoniale)17_Belgium_roads: road network data come from the platform Geofabrik of OpenStreetMap (http://download.geofabrik.de, accessed 08/21/2018) for Belgium18_Brabant_roads: Road network data come from the platform Geofabrik of OpenStreetMap (http://download.geofabrik.de, accessed 08/21/2018) for the province of Brabant.

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 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 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 data set is composed by 5 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

au2010_carto.geojson : categorized 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 5 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

au2010_carto.geojson : aires urbaines catégorisées 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