dataesr/scientific-paragraphs-categorization huggingface dataset:
One of the more controversial methods of contributing data to the OSM database is through the bulk import of suitably licensed geographic data. The pros and cons of taking a geographic dataset produced outside of OSM and importing it into the OSM database have been discussed by many authors  (Zielstra et al. , 2013) , and the issue remains a contentious one amongst the OSM community. One of the most powerful arguments against this bulk import is that it goes against the very ethos of OSM that data be collected or mapped by OSM contributors based on an ability to verify the quality of the data, ability itself founded on local knowledge, physical collection of the data or geographic expertise. Many examples of bulk import are available on the OSM wiki website 7 , with the TIGER data import of roads and highways into OSM United States and the CORINE LandCover map import into OSM France amongst the most well known and controversial.
Applications Using OSM Data In the introductory section of this chapter, we mentioned that, while much of the focus of OSM is on the maps and cartographic products derived from the OSM data, the core product of OSM is the spatial database. This second section will provide a comprehensive list of a number of projects, organisations, services, software and applications that make direct use of OSM data, with references and links provided at the end of the chapter. A number of such lists and descriptions are available on the Internet (e. g. on the OSM wiki 8 ), but, to the authors' knowledge, this is the first list provided in an academic paper. Due to the free and open availability of OSM data and the increasing popularity of OSM worldwide, it would be impossible to list all of the existing projects and applications.
Data Download Applications and Services Regardless of the types of applications and visualisations that can be produced with OSM, the applications and services that provide access to the data within the OSM database are arguably the most important part of the OSM's data architecture. Geofabrik is one of the best known providers of access to OSM data and provides access to continental-, national-and regional-sized data extracts 10 ; the data are uploaded very frequently (at least hourly) and are provided in a number of different formats. The OSM wiki provides access to the so-called Planet. osm file 11 , which is the entire OSM database contained in one very large XML or compressed format file. This file is updated every few days. The wiki page lists many mirror servers providing access to the Planet.
osm file, with many of these servers providing the file updated on an hourly basis. OSM also provides an API 12 that allows extracting and saving raw data from/to the OSM database. There are API calls to create, read, update and delete map data for OSM, and this provides software developers and applications with the most up-to-date data available. However, queries for very large amounts of data (such as city-or country-sized) are discouraged and disallowed. The Overpass API service 13 , with its popular frontend Overpass Turbo 14 , is a readonly API that allows access to selected parts of the OSM map database; clients send queries using a special API query language or using the graphical interface provided by Overpass Turbo. The Overpass API also allows programmatic calls for data extracts of arbitrary geographic size.
Education and Research Use of OSM The ability to access the entire OSM spatial database on an hourly basis or even more frequently has proved a great attraction for the research community over the past number of years  (Jokar Arsanjani et al. , 2015). There has been a steady increase year-on-year of the number of papers being produced by the academic community in the domain of VGI, and OSM forms a major component of this work. In 2015, one of the first edited volumes on OSM as a research topic was published  (Jokar Arsanjani et al. , 2015) ; the volume considered OSM's role in GIScience and contained a very wide range of research topics, from navigation and routing to data quality and visualisation. Similarly, two EU COST Actions focused on VGI that ran from 2012 to 2016, TD1202 'Mapping and the Citizen Sensor' (from where this volume comes) 21 and IC1203 'ENERGIC' 22 , have produced some excellent research around OSM.
From the opposite direction, there has been significant use of government data in OSM, with several high-profile data imports having been performed over the years. These imports are based on the imported data having an acceptable open data licence allowing the corresponding geodata to be inserted into the OSM database. The imports include: the TIGER (the Topologically Integrated Geographic Encoding and Referencing system) data, produced by the US Census Bureau, in the USA; plan. at in Austria; GeoBase as a complete map of Canada; and the CORINE Land Cover map in France. In 2013, New York City opened up many 'high-value datasets to the public, making it possible to use these data to improve OSM' 37 , facilitated and assisted by Mapbox 30. 'In return, New York City's GIS team is informed of changes made in OSM related to their datasets, which helps keep their map data current.
' This effectively made the New York City municipality a participant and contributor to OSM in the United States. MapGive 38 is an initiative of the US Department of State's Humanitarian Information Unit, 'mak  [ing]  it easy for new volunteers to learn to map and get involved in online tasks'. Portland's TriMet traffic authority uses OSM to power their multi-modal traffic planner 39. The Gendarmerie Nationale (one of the national police forces in France) uses OSM maps inside their police cars 40. The CROWDGOV report by  Haklay et al. (2014)  has a number of examples of governmental use of OSM around the world. There is still some reluctance by government agencies to use VGI and OSM as a complement to their own sources of spatial data  (Olteanu-Raimond et al. , 2017b) ; however, examples do exist, such as the French National Address Database (BAN), which 'associates each address listed on the French territory (25 million addresses) with its geographic coordinates' (the database 'does not contain any nominative data').
Visualisation of OSM Data From anecdotal evidence, visualisation of OSM data is certainly one of the most popular applications of OSM data. Visualisation of OSM data is facilitated by the flexible availability of the OSM data (see Section 2. 1) and the very wide range of visualisation tools available, which can natively process OSM data directly or from a spatial database. There is a vast number of examples, and we provide a small selection here for the purposes of illustrating the breadth of applications. OpenTopoMap 42 provides a topographic visualisation of OSM data combined with SRTM elevation data. The map tiles in OpenTopoMap are available for use as a web-map layer in other applications. OpenCycleMap 43 is an OSM rendering 'primarily aimed at showing information useful to cyclists'.
OSM Data Editors OSM is an openly accessible spatial database which any contributor can supply geodata to and whose existing data any contributor can also edit. It is therefore very important that software tools be available to support this editing work for contributors. The OSM wiki contains an extensive list of OSM data editing tools 56 and a comparison of their characteristics. In this section we outline five of the most famous and well known OSM editors. The iD editor 57 is a Web-based editor for OSM and is the editor that is integrated into the OSM homepage. The JOSM editor 3 is a Java editor for OSM and is considered an editor for skilled OSM contributors. It 'supports loading GPX tracks, background imagery and OSM data from local sources as well as from online sources and allows' direct editing of the OSM data; a number of plugins provide other advanced functions.
OSM-based Routing Services OSM-based routing services are software-based solutions that use the data in the OSM database for the purposes of generating routing and navigation solutions. Routing and navigation is possible when objects in OSM have attributes (tags) that are helpful in solving these problems. The ability to apply attributes from different thematic areas on the same object (such as a road or a street) means that different routing applications can be easily developed. The Open Source Routing Machine (OSRM) 61 is a C++ routing engine for finding 'shortest paths in road networks'. It supports car, bicycle and walk modes and is 'easily customized through profiles'. GraphHopper 62 is a company based in Germany focused on delivering the 'fastest possible routing algorithms' and 'privacy protection' using open source software for their customers.
Other Services In this section, we provide some links to other services that use OSM but do not necessarily fit neatly inside our classifications. In OSM, nodes that have specific tags are often called POI amongst contributors and users of OSM. There is no absolute set of tags that qualify as indicating a POI, but usually a POI will have tags related to amenities, such as buildings, shopping, education or buildings with cultural and historical significance. The OpenPoiMap 72 provides a map-based visualisation of all POI in OSM for any part of the world: POI are presented as individual layers, which can be turned on or off, and, based on what visualisation information the map provides, contributors can then edit the POI data directly in OSM using the links provided on the interface. The Places! service 73 attempts to present a visualisation of the analysis of patterns in place names within given countries based on the OSM database for those countries.
These problems include: very long ways (polylines); self-intersecting ways, polygons or polylines, which are represented by only one node; and polygons or polylines that have duplicate nodes contained within them. Taginfo 2 is a very popular Web-based application that displays up-to-date statistics about the tags used in the OSM database, e. g. which tags are used, how many times they are used, where a certain tag occurs, etc. Taginfo is particularly useful for finding problems with the keys or values in tags, the popularity of tags, where specific tags are used and which other tags are used in combination with them. The use of taginfo to find problems with tagging relates to its very comprehensive listing of the ranking of popularity/application of values to specific keys in tags. This can quickly allow an OSM expert to identify instances of an incorrect assignment of values in tags that has an overall effect on tag data quality.
Taginfo does not provide any information on errors relating to geometry or topology. Osmose 80 , an acronym for OpenStreetMap Oversight Search Engine, is a quality assurance tool available to detect issues in OSM data; it is also useful for integrating third-party datasets. It tries to detect anomalies in the data and then display them on an OSM map, from which contributors can fix or update them. Keep Right 81 is one of the oldest quality assurance tools in OSM. It displays automatically detected errors on the OSM map or in a list format, and it detects a very wide set of error types, including geometry errors, topological errors, attribution errors and other general OSM errors. MapRoulette 82 is a Web-based application that proposes challenges to fix errors in OSM. Each challenge represents a set of tasks, and OSM contributors can fix the errors by performing edits in OSM in the usual way.
The user (who needs to have an OSM account) must then provide an appropriate classification for this entity by choosing what he/she believes is correct from the list of classifications: grass, park, garden, forest and meadow. The JOSM Validator 84 'is a core feature of JOSM which checks and fixes invalid data' that have been contributed to OSM or are being contributed for the first time. The validator checks and fixes a wide variety of problems, including topological errors, unclosed polygons and overlapping areas. Academic research has produced a wide range of quality assessment and comparison tools for OSM  (Ostermann and Granell, 2017). One of the most recently published is that of  Brovelli et al. (2017) : this open source software tool provides an automated comparison of street network data in OSM with that in an authoritative dataset.
Games, Leisure and General Public Information In this final section of applications for OSM, we describe a mixture of applications that use OSM for the purposes of games, leisure or general public information. 'Collapse -The Division Game' 85 is a simulation game based on open datasets (including OSM data), created by Ubisoft to introduce the environment upon which the new online action game 'TomClancy's The Division' (for Windows, Playstation and Xbox) 86 is based. The user is the first person in the world infected with a virus, and the game realistically simulates the diffusion of the virus until the collapse of society; OSM data relating to health facilities, societal infrastructure and transportation are used in the simulation. The OSM game Kort 87 is very similar to MapRoulette 79 , with the exception that Kort drives a gamification approach to OSM error fixing.
Kort was developed for usage mainly on mobile devices but also works well on most browsers. For both solving tasks and checking existing solutions, points (so-called Koins) can be earned. The goal is to continually rise through the ranks of the high-score list. Additionally, players are also awarded medals for their efforts. At the time of writing, there are over 2,000 active players having solved almost 50,000 tasks. The solutions to tasks must be evaluated and accepted by other users before they are submitted to the OSM database. In a YouTube video 88 , an OSM contributor provides a video-based visualisation of the contribution of nodes to OSM over the period 2004-2016. Nodes in OSM that have had more editing activity on them are coloured using a heatmap approach. This timelapse video and many others listed on the OSM wiki 89 provide a very good high-level overview of how OSM has developed since its inception.
Conclusions and Discussion In this chapter, we have provided an overview of the OSM project. As mentioned in the introduction, OSM is probably the most famous example of VGI on the Internet today. Even at the time of writing (during the summer of 2016), the project continued to grow and expand, with over 2. 7M registered contributors/users and almost 3. 4B nodes of data, which made up almost 350M polygons and polylines. Around 37,000 contributors are active in OSM during a typical month. OSM can certainly claim to be the largest freely and openly accessible database of geographic data in the world. Indeed its rate of growth in terms of geographic data and frequency of contributions and editing brings OSM into the realm of geographic big data  (Leonelli, 2014). When one considers the extended OSM ecosystem of open source software, data download services, data visualisation services, wiki help systems, mailing lists and forums, OSM serves as a very suitable starting point for any discussion on VGI.
Indeed one could speculate on how VGI would have developed if OSM had been absent from this space. This chapter has attempted to give the reader who is new to OSM an introduction to the OSM ecosystem while providing the reader familiar with OSM an overview of where OSM currently stands in the world of VGI. In the remaining chapters of this book, OSM will be mentioned and discussed in many different ways. In Chapter 4,  Touya et al. (2017)  address the challenges of automated mapmaking using VGI as the input data, and the authors consider OSM as a key source, but not the only source, of this VGI data. Chapter 2,  See et al. (2017)  has already indicated that there are many sources of VGI available today. While OSM is open data and is licensed under the Open Data Commons Open Database License (ODbL), there are privacy and ethical issues around the reuse of OSM data.
As discussed earlier in this chapter, OSM has a very flexible and easy-to-understand approach to the contribution of new geographic data or editing of existing data in the OSM database. Chapter 10 considers best practices for VGI data collection, and  Minghini et al. (2017)  propose in that chapter that the lack of protocols and the flexibility of contribution is not necessarily a good thing in terms of producing consistently high-quality VGI data. Chapter 11  (Bastin et al. , 2017)  considers VGI data management and suggests ways in which OSM can be integrated into the so-called Semantic Web, where all OSM's data would be converted to Linked Data. Finally, Chapter 13  (Olteanu-Raimond et al. , 2017a)  discusses VGI and the role of NMAs, with OSM often seen as a rival or competitor to the geographic data services provided by these agencies.
The Future of OSM OSM's greatest strength will always be its huge pool of contributors. Thousands of these contributors have collected and generated some of the world's best street and topographic data without expensive teams of professional surveyors or world-class equipment. As the world and the urban and natural environment change every day, OSM contributors have the ability to depict this changing world in a map and a database that belong to them. OSM may not yet have the advanced types of features that Google Maps has -street-view images, multimodal navigation, social recommendations, etc. -but it may soon have. Mapillary 96,97 , which is a service for crowdsourcing street-level photographs using smartphones and computer vision, has almost 70 million geotagged street-level photographs at the time of writing.
Mapillary shares the open data ethos of OSM and they can work well together  (Juhász and Hochmair, 2016). Very similarly, efforts are in place to link OSM elements with their corresponding Wikipedia pages and Wikidata items. As an example, the WTOSM 98 (Wikipedia To OSM) service developed by the Italian OSM community automatically identifies Wikipedia pages that can be linked (by means of tags) to OSM elements. Mature services such as OpenRouteService provide navigation services based wholly on OSM's database. One of the factors in the evolution of OSM over the past decade or so has been the ability of the project to adapt and expand in the face of technological advancements in other areas of ICT and Open Source Software. Web service access to the OSM database or its mirrors has improved and is very stable, allowing developers to build an array of applications using the data directly from the database.
There are some challenges for OSM going forward. These challenges are a mixture of factors based on the social and technological aspects of VGI  (Mooney, 2015). Contributors can make edits to the OSM global database without any real controls or moderation at the point of contribution. Despite the fact that there are many applications available for an a posteriori quality check (see Section 2. 7), as long as edits can be made without initial controls the issue of OSM data quality will remain a contentious one. Relatively unknown contributors from an unknown crowd supplying geospatial data is a concern to end users and stakeholders such as NMAs, government agencies and commercial companies. There have been many instances in the past where large amounts of OSM data have been deleted by new or inexperienced contributors.
Some authors have considered the problem of automated detection of instances of vandalism and of the purposeful deletion of data in OSM  (Neis et al. , 2012). Many local OSM communities have long debated the wish and need to implement tools for checking and approving contributions (e. g. by more experienced contributors or by the community itself). However, such an implementation would be clearly against the very same nature of the OSM project, and no formal actions are yet in place in this regard. Several academic studies have shown that for specific regions of the world, OSM has reached a very high and mature level of completeness and spatial accuracy compared to data from sources such as NMAs  (Dorn et al. , 2015). One of the major challenges will be to sustain the contributor motivation for editing and maintaining the OSM database into the future  (Budhathoki and Haythornthwaite, 2012).
Every day sees less white space or empty places on the OSM map. Similar scenarios are being observed in Wikipedia  (Jankowski-Lorek et al. , 2016). The task of being an OSM contributor is changing from that of being the contributor of brand new geodata to OSM to that of map gardening  (McConchie, 2016; Sinton, 2016) ; in this latter case, contributors are not necessarily involved in contributing new material to OSM but are attending to the upkeep and update of the existing geometry and attribute data (tags) in the database. As geolocation is further embedded into social media, user-generated content on the Internet, etc. , issues of privacy and ethics can be raised  (Blatt, 2015) , and the work outlined in Chapter 6 of this book  (Mooney et al. , 2017) , highlighting these problems in relation to VGI, will become critical; currently, very little work has been undertaken by the research community into privacy and ethics in VGI.
Multiple Criteria Decision Method We stated in Section 3. 1 that LoD can be affected by a combination of five factors, all of which can be measured in a geographic dataset but are hardly comparable or can hardly be added. Multi-criteria decision methods are computational techniques that allow decision-making based on several criteria in those cases where a simple numerical value such as a mean is not a valid solution  (Roy, 2005). Touya and Brando-Escobar (2013)  propose a multi-criteria.
g. people who provide traffic data via their mobile phone; • open mappers, e. g. those contributing to initiatives such as OSM; • and mappers that would be motivated by financial incentives, e. g. through using Amazon's Mechanical Turk. This typology already provides some insights into possible motivational factors such as interest in the subject or material gain. The open mappers were identified as being the largest group after passive mappers and one that is increasing in size over time. Although their motivations are thought to be altruistic and related to building and using open datasets as a public good  (Goodchild, 2007; Heipke, 2010) , the range of motivations driving the group of open mappers is much more complex and nuanced  (Budhathoki and Haythornthwaite, 2012) , as outlined in the next section.
Gamification has also been shown to help motivate participation in a citizen science application such as Project Budburst, which developed the Biotracker app for gathering phenology data: use of technology such as smartphones, coupled with competitive elements such as badges and leaderboards, was shown to appeal to the younger 'Millennial' audience  (Bowser et al. , 2013). A number of game apps have been built for gathering OSM data, e. g. AddressHunter, which is a role playing game that also involves adding addresses to the OSM database, and Kort Game, for adding new features to OSM  (OSM, 2013). Motivation is also clearly linked to maintaining participation in the longer term. The use of different incentives can be a powerful way to achieve this. Reputation and confidence building measures can be effective ways to motivate volunteers.