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.

The ScaleMaster diagram of Brewer and Buttenfield, "where the scaleLine replaces the timeLine", is a formal tool (Excel sheets) designed to formalize the rules for manual map design and "emphasize changes to the map display" . Inspired by Brewer and Buttenfield, we use ScaleMaster to standardize and formalize changes while zooming and exploring each of pan-scalar map (OSM,Google Maps,Scan IGN). In our methodology, however, we go a step further. The timeline of exploration is also examined in addition to the scaleline of zooming. We focus on map design practices that account for pan-scalar map exploration. For example, we account for generalization changes between scales based on empirically or theoretically justifiable reasons.

we use ScaleMaster to analyze particular and common geographic entities in the maps (including rivers, urban areas, bus stations, and administrative borders) representing but a fraction of all map ontologies (e.g., water, roads, transportation networks, relief, points-of-interest, vegetation, administrative districts). We constructed a ScaleMaster for each of the three pan-scalar maps (OSM, Google Map, Scan IGN).

Our hope is that this first analysis, and the resulting categories below, will lead to critique, comment, and iterative improvement in the future. In other words, our initial findings are just that – outcomes that further exploration on pan-scalar maps can add to, revise, and improve upon.

WMTS-ORTOFOTO_900913 view service is provided as a public view service for the Orthophoto of the Czech Republic data. To optimize the speed of the service, the data are provided in the form of map tiles. The service covers with orthophoto data complete applicable scale interval, i.e. also small scales. The service fulfils Technical guidance for INSPIRE view services v. 3.11 and simultaneously fulfils the OGC WMTS 1.0.0 standard. This service is optimized for use in applications exploiting WGS 84 / Pseudo-Mercator (EPSG 3857 alias 900913) as the implicit datum and requiring Google Maps/Bing Maps/OSM standards as for scale series.

[Keywords] Market include Hexagon, GIS Cloud, Mapbox, OpenStreetMap, Apple Maps

carte réalisée à partir de la base OpenStreetMap avec un style proche de la carte Google Maps

This map shows the power generators for the whole world. The power lines have been reviewed for positional accuracy using google satellite maps. Most of the lines checked on the map, seem to correspond with the actual location lines as confirmed by high resolution aerial images from google satellite maps.Limitations on the dataset include incompleteness in certain areas, and less information on the voltage capacity of some of the lines. This dataset was extracted from the OpenStreetMap initiative. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF).© OpenStreetMap contributorshttp://www.openstreetmap.org/copyright

carte réalisée à partir de la base OpenStreetMap avec un style proche de la carte Google Maps

This dataset is a collection of disorientation stories experienced during the use of a multi-scale topographic map (such as Google Maps, Bing Maps, OpenStreetMap, the ones produced by national mapping agencies), either on a phone, or on a computer. These experiences come from different discussions the authors had with colleagues, friends and family members about being disoriented while exploring a pan-scalar map. This collection is an on-going work, and new versions will be released when other stories will be collected.

The stories are gathered in the file named disorientation_stories_table.xlsx, with a textual report of the story in one of the columns of the table. The other files are images illustrating the different stories.

Map Data Services market has a significant presence globally, with a market size valued at XXX million in 2025. It is projected to expand at a CAGR of XX% during the forecast period, reaching XXX million by 2033. The growth of the market is primarily driven by the increasing demand for accurate and reliable map data for various applications such as navigation, location-based services, and urban planning. Additionally, the rise of autonomous vehicles and the adoption of advanced technologies like augmented reality and virtual reality are further contributing to the demand for map data. The key players in the Map Data Services market include Google, WikiMapia, Apple Maps, Here, Bing Maps, Navinfo, TomTom, Mapbox, Esri, AutoNavi, Baidu Apollo, Sanborn, Yandex, Azure Maps, OpenStreetMap, and ArcGIS. These companies offer a wide range of map data products and services to meet the diverse needs of various industries and consumers. The market is segmented by application, type, and region, providing a comprehensive overview of the industry landscape and competitive dynamics. North America, Europe, and Asia Pacific are the major regional segments of the market, with North America holding a significant share due to the presence of major technology companies and the adoption of advanced technologies.

A forecast map layer covering the UK showing precipitation. Precipitation is any product of the condensation of atmospheric water vapour that falls under gravity. The main forms of precipitation include drizzle, rain, sleet, snow and hail. Single tile map layer images are provided three hourly from T+0 to T+36. The map layer is provided without a map, the boundary box for this image is 48 to 61 degrees north and 12 degrees west to 5 degrees east. The image layers are currently made available in a Mercator projection, it is the same projection used by Bing maps, OpenStreetMap, Google maps, MapQuest, Yahoo maps, and others.

The map of the city of Granollers is a set of cartographic files that represent spatial information related to the location of the streets, services and facilities of the city, through a geoportal that uses Google Maps and OpenStreetMap as base maps. The addresses are shown in dot form and for each case, and complementary information is displayed interactively.

The OnMapGaze dataset includes both experimental and analyzed gaze data collected during the observation of different cartographic backgrounds used in five online map services, including Google Maps, Wikimedia, Bing Maps, ESRI, and OSM at three different zoom levels (12z, 14z, & 16z).

A full description of the OnMapGaze dataset is cited in the paper below:

Liaskos, D., & Krassanakis, V. (2024). OnMapGaze and GraphGazeD: A Gaze Dataset and a Graph-Based Metric for Modeling Visual Perception Differences in Cartographic Backgrounds Used in Online Map Services. Multimodal Technologies and Interaction, 8(6). https://doi.org/10.3390/mti8060049

carte réalisée à partir de la base OpenStreetMap avec un style proche de la carte Google Maps

Number and area of urban parks before and after harmonization obtained by GMaps and OSM tools for the 16 cities with official spatial data.

World elevation dataset

High resolution dataset containing the world elevation above the sea level in meters. See python example to get the estimated elevation from a coordinate.

  Info

This dataset comprises global elevation data sourced from ASTER GDEM, which has been compressed and retiled for efficiency. The retiled data adheres to the common web map tile convention used by platforms such as OpenStreetMap, Google Maps, and Bing Maps, providing compatibility with zoom… See the full description on the dataset page: https://huggingface.co/datasets/Upabjojr/elevation-data-ASTER-compressed-retiled.

Grid administrative map of Austria basemap.at is an internet-enabled base map of Austria, based on the geodata of the countries and their partners, freely available and performant. basemap.at is the result of a cooperation project of the nine Austrian countries (geoland.at), ITS Vienna Region/GIP.at operator, as well as the Technical University of Vienna and Synergis. Co-financed by the BMVIT, the basis for an administrative card freely available on the Internet from 2014 onwards was established by the nine countries, which serves both as a basis for numerous administrative procedures and is freely available for use in Austria for any private or commercial use in accordance with the framework of Open Government Data. basemap.at Raster is a grid map in the form of a pre-generated tile cache, in which Web Mercator Auxiliary Sphere and thus compatible with the common worldwide base maps such as those of OpenStreetMap, Google Maps and Bing Maps. For East Austria, the basemap.at grid is also offered in the Gauss-Krüger Projection M34 (EPSG:31256). Please note the terms of use/appointment, see further metadata.

This multi-spectral satellite image data set is associated with our recent work on analyzing and predicting urban land use forms in East Africa using OpenStreetMap data, satellite imagery, and Convolutional Neural Networks.

The images were extracted using an automated Python script from Google Maps Static API, based on sample locations in four East African capital cities namely Kampala, Nairobi, Dar es Salaam, and Kigali.

Other data sets associated with this work, that is, ESRI shapefiles for administrative level 1 and OpenStreetMap data for the named cities may be downloaded directly from the respective URLs provided in the manuscript.

This global accessibility map enumerates land-based travel time to the nearest densely-populated area for all areas between 85 degrees north and 60 degrees south for a nominal year 2015. Densely-populated areas are defined as contiguous areas with 1,500 or more inhabitants per square kilometre or a majority of built-up land cover types coincident with a population centre of at least 50,000 inhabitants. This map was produced through a collaboration between MAP (University of Oxford), Google, the European Union Joint Research Centre (JRC), and the University of Twente, Netherlands.The underlying datasets used to produce the map include roads (comprising the first ever global-scale use of Open Street Map and Google roads datasets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders. These datasets were each allocated a speed or speeds of travel in terms of time to cross each pixel of that type. The datasets were then combined to produce a "friction surface"; a map where every pixel is allocated a nominal overall speed of travel based on the types occurring within that pixel. Least-cost-path algorithms (running in Google Earth Engine and, for high-latitude areas, in R) were used in conjunction with this friction surface to calculate the time of travel from all locations to the nearest (in time) city. The cities dataset used is the high-density-cover product created by the Global Human Settlement Project. Each pixel in the resultant accessibility map thus represents the modelled shortest time from that location to a city. Authors: D.J. Weiss, A. Nelson, H.S. Gibson, W. Temperley, S. Peedell, A. Lieber, M. Hancher, E. Poyart, S. Belchior, N. Fullman, B. Mappin, U. Dalrymple, J. Rozier, T.C.D. Lucas, R.E. Howes, L.S. Tusting, S.Y. Kang, E. Cameron, D. Bisanzio, K.E. Battle, S. Bhatt, and P.W. Gething. A global map of travel time to cities to assess inequalities in accessibility in 2015. (2018). Nature. doi:10.1038/nature25181

Processing notes: Data were processed from numerous sources including OpenStreetMap, Google Maps, Land Cover mapping, and others, to generate a global friction surface of average land-based travel speed. This accessibility surface was then derived from that friction surface via a least-cost-path algorithm finding at each location the closest point from global databases of population centres and densely-populated areas. Please see the associated publication for full details of the processing.

Source: https://map.ox.ac.uk/research-project/accessibility_to_cities/

Based on WFP layer, we cross-checked it with other layers such as OSM, Google Maps and Where We Fly - Mission Aviation Fellowship - Haiti.

במפה הגלובלית הזו של נגישות מוצג זמן הנסיעה היבשתי (בדקות) לבית החולים או למרפאה הקרובים ביותר בכל האזורים שבין 85 מעלות צפון ל-60 מעלות דרום, בשנת 2019. הוא כולל גם את זמן הנסיעה 'הליכה בלבד', באמצעות אמצעי תחבורה לא ממונעים בלבד. כדי ליצור את האוסף המלא ביותר של מיקומי מוסדות בריאות עד כה, נעזרנו במאמצים משמעותיים לאיסוף נתונים של OpenStreetMap, ‏ Google Maps וחוקרים אקדמיים. המפה הזו נוצרה בשיתוף פעולה בין MAP (אוניברסיטת אוקספורד), Telethon Kids Institute (פרת', אוסטרליה), Google והאוניברסיטה של Twente, הולנד. הפרויקט הזה מבוסס על עבודה קודמת שפורסמה על ידי Weiss et al‏ 2018 (doi:10.1038/nature25181). Weiss et al (2018) השתמשו במערכי נתונים של כבישים (השימוש הראשון בעולם בקנה מידה גלובלי במערכי הנתונים של Open Street Map ושל Google Roads), מסילות ברזל, נהרות, אגמים, אוקיינוסים, תנאים טופוגרפיים (שיפוע וגובה), סוגי כיסוי פני השטח וגבולות לאומיים. לכל מערך נתונים הוקצו מהירויות נסיעה, במונחים של זמן לחציית כל פיקסל מהסוג הזה. לאחר מכן, ערכות הנתונים שולבו כדי ליצור 'פני שחיקה': מפה שבה לכל פיקסל מוקצה מהירות נסיעה נומינלית כוללת על סמך הסוגים שמתרחשים בפיקסל הזה. בפרויקט הנוכחי, נוצרה פני שטח של חיכוך מעודכנת כדי לשלב שיפורים שבוצעו לאחרונה בנתוני הכבישים ב-OSM. השתמשנו באלגוריתמים של נתיב בעלות הנמוכה ביותר (שפועלים ב-Google Earth Engine ובאזורים של קו הרוחב הגבוה ב-R) בשילוב עם פני השטח של החיכוך כדי לחשב את זמן הנסיעה מכל המיקומים למוסד הרפואי הקרוב ביותר (מבחינת זמן). מערך הנתונים של מוסדות הבריאות מבוסס על נתוני מיקום משני מסדי הנתונים הגלובליים הגדולים ביותר: (1) נתוני OSM שנאספו והועמדו לשימוש באתר www.healthsites.io, ו-(2) נתונים שחולצו ממפות Google. מערכי הנתונים הגלובליים הורחבו במיקומי מתקנים ברמת היבשת, שפורסמו לאחרונה לאפריקה ולאוסטרליה. כדי לאפשר השוואות בין מקורות הנתונים, השתמשו רק במתקנים שהוגדרו כבתי חולים ומרפאות. נקודות מרובות שנמצאו באותו פיקסל מוזגו כדי להתאים לרזולוציה של הניתוח, כפי שהוגדרה על ידי הייצוג הממורץ שנבחר של פני כדור הארץ. לכן, כל פיקסל במפת הנגישות שמתקבלת מייצג את משך הזמן הקצר ביותר (בדקות) מהמיקום הזה לבית חולים או למרפאה. הזיכויים על מערכי נתונים של מקורות מתוארים במאמר המצורף.