Special Issue "OpenStreetMap as A Multi-Disciplinary Nexus: Perspectives, Practices and Procedures"

Special Issue Editors

Dr. A. Yair Grinberger
Website
Guest Editor
Department of Geography, The Hebrew University of Jerusalem, Mount Scopus, 9190501, Jerusalem, Israel
Interests: GIScience; volunteered geographic information; geographical representation; social aspects of geo-information; mobility analysis; spatial agent-based models; urban dynamics
Dr. Marco Minghini

Guest Editor
European Commission-Joint Research Centre (JRC), Via Enrico Fermi 2749, 21027 Ispra (VA), Italy
Interests: crowdsourcing, volunteered geographic information and OpenStreetMap; land cover/land use validation; open source geospatial software; geospatial interoperability; Spatial Data Infrastructures
Special Issues and Collections in MDPI journals
Dr. Peter Mooney
Website
Guest Editor
Department of Computer Science, Maynooth University, Maynooth, Co. Kildare, W23 F2H6, Ireland
Interests: volunteered geographic information; open source geospatial software; user-generated geodata; geocomputation; citizen science; spatial databases; web-based GIS
Dr. Levente Juhász
Website
Guest Editor
GIS Center, Florida International University, United States
Interests: GIScience; user-generated geodata; spatial analysis; geocomputation; citizen science; volunteered geographic information; geospatial web
Dr. Godwin Yeboah
Website
Guest Editor
Institute for Global Sustainable Development, University of Warwick, Coventry CV4 7AL, UK
Interests: GIScience; citizen science; spatial analysis; geomatics; open-source; geocomputation; human–environment interactions; urban informatics; sustainable development

Special Issue Information

Dear Colleagues,

We are excited to invite you to submit a research paper to “OpenStreetMap as a multi-disciplinary nexus: Perspectives, Practices and Procedures”, a Special Issue of the ISPRS International Journal of Geo-Information. The aim of this Special Issue is to showcase both the ongoing innovation and the maturity of scientific investigations and research into OpenStreetMap, demonstrating how, as a research object, it converges multiple research areas together. Collecting contributions from multiple disciplines and domains, the Special Issue will show how the sum total of investigations of issues like VGI, geo-information, and geo-digital processes and representations can shed light on the relations between crowds, real-world applications, technological developments, and scientific research.

This Special Issue is primarily aimed at collecting papers that extend the research works presented in the Academic Track of State of the Map 2019, held in Heidelberg (Germany) on September 21–23, 2019. However, other original submissions aligned with the area of research are also highly welcome.

We expect empirical, methodological, or conceptual contributions addressing any scientific aspect related to OpenStreetMap, in particular, but not limited, to the following:

  • Extrinsic and intrinsic quality assessment of OpenStreetMap data
  • Analysis of contribution patterns in OpenStreetMap
  • Interactions between OpenStreetMap and other data sources
  • Analysis/comparison of available software for scientific purposes related to OpenStreetMap
  • New approaches to facilitate or improve data collection in OpenStreetMap (e.g., through gamification or citizen science approaches)
  • Bridging the communities: Creating better connections and collaborations between the scientific community and the OpenStreetMap community
  • Open research problems in OpenStreetMap and challenges for the scientific community
  • Cultural, political, and organizational aspects of data production and usage practices in OpenStreetMap
  • Literature reviews and theoretical papers on any of the listed topics or topics related to the scope of the Special Issue

Dr. A. Yair Grinberger
Dr. Marco Minghini
Dr. Peter Mooney
Dr. Levente Juhász
Dr. Godwin Yeboah
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • OpenStreetMap
  • Volunteered geographic information
  • User-generated geospatial content
  • Contribution patterns
  • Quality assessment
  • Collaborative mapping
  • Data conflation

Published Papers (6 papers)

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Research

Open AccessArticle
Worldwide Detection of Informal Settlements via Topological Analysis of Crowdsourced Digital Maps
ISPRS Int. J. Geo-Inf. 2020, 9(11), 685; https://doi.org/10.3390/ijgi9110685 - 16 Nov 2020
Abstract
The recent growth of high-resolution spatial data, especially in developing urban environments, is enabling new approaches to civic activism, urban planning and the provision of services necessary for sustainable development. A special area of great potential and urgent need deals with urban expansion [...] Read more.
The recent growth of high-resolution spatial data, especially in developing urban environments, is enabling new approaches to civic activism, urban planning and the provision of services necessary for sustainable development. A special area of great potential and urgent need deals with urban expansion through informal settlements (slums). These neighborhoods are too often characterized by a lack of connections, both physical and socioeconomic, with detrimental effects to residents and their cities. Here, we show how a scalable computational approach based on the topological properties of digital maps can identify local infrastructural deficits and propose context-appropriate minimal solutions. We analyze 1 terabyte of OpenStreetMap (OSM) crowdsourced data to create worldwide indices of street block accessibility and local cadastral maps and propose infrastructure extensions with a focus on 120 Low and Middle Income Countries (LMICs) in the Global South. We illustrate how the lack of physical accessibility can be identified in detail, how the complexity and costs of solutions can be assessed and how detailed spatial proposals are generated. We discuss how these diagnostics and solutions provide a multiscalar set of new capabilities—from individual neighborhoods to global regions—that can coordinate local community knowledge with political agency, technical capability, and further research. Full article
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Open AccessArticle
Leveraging OSM and GEOBIA to Create and Update Forest Type Maps
ISPRS Int. J. Geo-Inf. 2020, 9(9), 499; https://doi.org/10.3390/ijgi9090499 - 21 Aug 2020
Abstract
Up-to-date information about the type and spatial distribution of forests is an essential element in both sustainable forest management and environmental monitoring and modelling. The OpenStreetMap (OSM) database contains vast amounts of spatial information on natural features, including forests (landuse=forest). The [...] Read more.
Up-to-date information about the type and spatial distribution of forests is an essential element in both sustainable forest management and environmental monitoring and modelling. The OpenStreetMap (OSM) database contains vast amounts of spatial information on natural features, including forests (landuse=forest). The OSM data model includes describing tags for its contents, i.e., leaf type for forest areas (i.e., leaf_type=broadleaved). Although the leaf type tag is common, the vast majority of forest areas are tagged with the leaf type mixed, amounting to a total area of 87% of landuse=forests from the OSM database. These areas comprise an important information source to derive and update forest type maps. In order to leverage this information content, a methodology for stratification of leaf types inside these areas has been developed using image segmentation on aerial imagery and subsequent classification of leaf types. The presented methodology achieves an overall classification accuracy of 85% for the leaf types needleleaved and broadleaved in the selected forest areas. The resulting stratification demonstrates that through approaches, such as that presented, the derivation of forest type maps from OSM would be feasible with an extended and improved methodology. It also suggests an improved methodology might be able to provide updates of leaf type to the OSM database with contributor participation. Full article
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Open AccessArticle
Using OpenStreetMap Data and Machine Learning to Generate Socio-Economic Indicators
ISPRS Int. J. Geo-Inf. 2020, 9(9), 498; https://doi.org/10.3390/ijgi9090498 - 21 Aug 2020
Cited by 1
Abstract
Socio-economic indicators are key to understanding societal challenges. They disassemble complex phenomena to gain insights and deepen understanding. Specific subsets of indicators have been developed to describe sustainability, human development, vulnerability, risk, resilience and climate change adaptation. Nonetheless, insufficient quality and availability of [...] Read more.
Socio-economic indicators are key to understanding societal challenges. They disassemble complex phenomena to gain insights and deepen understanding. Specific subsets of indicators have been developed to describe sustainability, human development, vulnerability, risk, resilience and climate change adaptation. Nonetheless, insufficient quality and availability of data often limit their explanatory power. Spatial and temporal resolution are often not at a scale appropriate for monitoring. Socio-economic indicators are mostly provided by governmental institutions and are therefore limited to administrative boundaries. Furthermore, different methodological computation approaches for the same indicator impair comparability between countries and regions. OpenStreetMap (OSM) provides an unparalleled standardized global database with a high spatiotemporal resolution. Surprisingly, the potential of OSM seems largely unexplored in this context. In this study, we used machine learning to predict four exemplary socio-economic indicators for municipalities based on OSM. By comparing the predictive power of neural networks to statistical regression models, we evaluated the unhinged resources of OSM for indicator development. OSM provides prospects for monitoring across administrative boundaries, interdisciplinary topics, and semi-quantitative factors like social cohesion. Further research is still required to, for example, determine the impact of regional and international differences in user contributions on the outputs. Nonetheless, this database can provide meaningful insight into otherwise unknown spatial differences in social, environmental or economic inequalities. Full article
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Open AccessArticle
Change Detection from Remote Sensing to Guide OpenStreetMap Labeling
ISPRS Int. J. Geo-Inf. 2020, 9(7), 427; https://doi.org/10.3390/ijgi9070427 - 02 Jul 2020
Abstract
The growing amount of openly available, meter-scale geospatial vertical aerial imagery and the need of the OpenStreetMap (OSM) project for continuous updates bring the opportunity to use the former to help with the latter, e.g., by leveraging the latest remote sensing data in [...] Read more.
The growing amount of openly available, meter-scale geospatial vertical aerial imagery and the need of the OpenStreetMap (OSM) project for continuous updates bring the opportunity to use the former to help with the latter, e.g., by leveraging the latest remote sensing data in combination with state-of-the-art computer vision methods to assist the OSM community in labeling work. This article reports our progress to utilize artificial neural networks (ANN) for change detection of OSM data to update the map. Furthermore, we aim at identifying geospatial regions where mappers need to focus on completing the global OSM dataset. Our approach is technically backed by the big geospatial data platform Physical Analytics Integrated Repository and Services (PAIRS). We employ supervised training of deep ANNs from vertical aerial imagery to segment scenes based on OSM map tiles to evaluate the technique quantitatively and qualitatively. Full article
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Open AccessArticle
Quality Verification of Volunteered Geographic Information Using OSM Notes Data in a Global Context
ISPRS Int. J. Geo-Inf. 2020, 9(6), 372; https://doi.org/10.3390/ijgi9060372 - 06 Jun 2020
Abstract
Although the data obtained from volunteered geographic information (VGI) are inherently different from public surveys, the quantity of the data are vast and the quality of the data are often poor. To improve the quality of VGI data, the positional accuracy and diversity [...] Read more.
Although the data obtained from volunteered geographic information (VGI) are inherently different from public surveys, the quantity of the data are vast and the quality of the data are often poor. To improve the quality of VGI data, the positional accuracy and diversity and interaction of the number of users involved in the regional generation of the data are important. This research proposes a new approach for the accumulation of OpenStreetMap (OSM) data by using OSM Notes and attempts to analyze the geographical distribution and the characteristics of the contents of the contributions, quantitatively and qualitatively. Subsequently, the results demonstrated regional differences in OSM Notes, but it provided users with an understanding of the new features of quality management in OSM, even in regions where OSM activities are not necessarily active. In addition, it was also possible to discover new factors such as the time transition required for the correction and contribution of anonymous users. These results are expected to serve as a tool for users to communicate with each other to resolve data bugs that exist in OSM and provide future researchers with examples of user interaction in global OSM activities. Full article
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Open AccessEditor’s ChoiceArticle
Cartographic Vandalism in the Era of Location-Based Games—The Case of OpenStreetMap and Pokémon GO
ISPRS Int. J. Geo-Inf. 2020, 9(4), 197; https://doi.org/10.3390/ijgi9040197 - 26 Mar 2020
Cited by 4
Abstract
User-generated map data is increasingly used by the technology industry for background mapping, navigation and beyond. An example is the integration of OpenStreetMap (OSM) data in widely-used smartphone and web applications, such as Pokémon GO (PGO), a popular augmented reality smartphone game. As [...] Read more.
User-generated map data is increasingly used by the technology industry for background mapping, navigation and beyond. An example is the integration of OpenStreetMap (OSM) data in widely-used smartphone and web applications, such as Pokémon GO (PGO), a popular augmented reality smartphone game. As a result of OSM’s increased popularity, the worldwide audience that uses OSM through external applications is directly exposed to malicious edits which represent cartographic vandalism. Multiple reports of obscene and anti-semitic vandalism in OSM have surfaced in popular media over the years. These negative news related to cartographic vandalism undermine the credibility of collaboratively generated maps. Similarly, commercial map providers (e.g., Google Maps and Waze) are also prone to carto-vandalism through their crowdsourcing mechanism that they may use to keep their map products up-to-date. Using PGO as an example, this research analyzes harmful edits in OSM that originate from PGO players. More specifically, this paper analyzes the spatial, temporal and semantic characteristics of PGO carto-vandalism and discusses how the mapping community handles it. Our findings indicate that most harmful edits are quickly discovered and that the community becomes faster at detecting and fixing these harmful edits over time. Gaming related carto-vandalism in OSM was found to be a short-term, sporadic activity by individuals, whereas the task of fixing vandalism is persistently pursued by a dedicated user group within the OSM community. The characteristics of carto-vandalism identified in this research can be used to improve vandalism detection systems in the future. Full article
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