2. User Generated Spatial Content
3. User Generated Spatial Content—Integrator Model
3.1. Minimum Requirements and Relevant Initiatives
- Type of spatial context: In this matter we found two main types of spatial resolution: places and coordinates (latitude and longitude). Places are not accurate and sometimes can be very vague in terms of spatial location . For instance, when one mentions the name of a city, there is no accurate position in that city. Coordinates refer to a location with much more accuracy and therefore are of more interest for this study.
- Type of spatial phenomena: landscape, user position, highly dynamic phenomena (natural, such as fires, tornados, etc., or artificial, such as cars, animals, people, etc.), and static entities (buildings, roads, farms). User position and highly dynamic phenomena are not of interest for this study because they do not represent physical aspects of the earth.
- Type of data: text, photos, and geometries. Text events, when georeferenced by latitude and longitude coordinates or similar, can be very precise and rich in terms of geographical information, but more research that is outside the scope of this study is needed to extract meaningful information from messages/descriptions. Photos, when georeferenced by latitude and longitude coordinates, are very useful as they provide an image of the location. Photos georeferenced by places, as mentioned in the previous point, can have a very imprecise location. Geometries are usually georeferenced by their coordinates representing precise geographic data.
- Type of access: no public access, access using public APIs, access using private API, and access using direct URLs to the photos. Some initiatives, usually held by private companies, do not provide public access to stored data or require users to pay a fee to use their private API. Public APIs are available free of charge and manage privacy issues internally, so by using them only publically available content will be accessed. In this model only public APIs are considered.
- Type of data license: Open Data Commons Open Database License (ODbL), license to public use, and license that belongs to the contributor, among others, are some of the types of data licenses used. It is important to note that our model will use only publically available data and will not store or commercially exploit the data used.
- Type of coverage: local, regional, or global. Local coverage is more related with a small portion of the Earth, like a country or a region inside a country. Regional coverage is more connected with areas covering groups of countries or continents. Global coverage is associated with the entire globe. Depending on the type of coverage of the LULC being produced and the area of the Earth being classified, some initiatives can be more interesting than others (e.g., if the working area is Portugal, UGsC data covering Ireland will not be of interest).
3.2. Structural Similarities and Dissimilarities among the Initiatives Selected
3.3. Model Architecture
4. Prototype Development and Implementation
4.1. Definition of Use Cases
4.2. Architecture and Implementation
5. Results and Discussion
5.1. The Model in Action
5.2. Challenges and Limitations
5.3. Current Status and Future Developments
Conflicts of Interest
- Goodchild, M.; Glennon, J.A. Crowdsourcing geographic information for disaster response: A research frontier. Int. J. Digit. Earth 2010, 3, 231–241. [Google Scholar] [CrossRef]
- Goodchild, M. Commentary: Whither VGI? GeoJournal 2008, 72, 239–244. [Google Scholar] [CrossRef]
- Sui, D.; Goodchild, M.; Elwood, S. Volunteered geographic information, the exa flood, and the growing digital divide. In Crowdsourcing Geographic Knowledge; Sui, D., Elwood, S., Goodchild, M., Eds.; Springer: Berlin/Heidelberg, Germany, 2013; pp. 1–12. [Google Scholar]
- Estima, J.; Painho, M. Exploratory analysis of OpenStreetMap for land use classification. In Proceedings of the Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information, Orlando, FL, USA, 5 November 2013.
- See, L.; Comber, A.; Salk, C.; Fritz, S.; Velde, M.V.D.; Perger, C.; Schill, C.; McCallum, I.; Kraxner, F.; Obersteiner, M. Comparing the quality of crowdsourced data contributed by expert and non-experts. PLoS ONE 2013. [Google Scholar] [CrossRef] [PubMed]
- Pultar, E.; Raubal, M.; Cova, T.J.; Goodchild, M.F. Dynamic GIS case studies: Wildfire evacuation and volunteered geographic information. Trans. GIS 2009, 13, 85–104. [Google Scholar] [CrossRef]
- Zook, M.; Graham, M.; Shelton, T.; Gorman, S. Volunteered geographic information and crowdsourcing disaster relief: A case study of the haitian earthquake. World Med. Health Policy 2010, 2, 6–32. [Google Scholar] [CrossRef]
- Mooney, P.; Corcoran, P.; Winstanley, A. A study of data representation of natural features in OpenStreetMap. In Proceedings of the 6th GIScience International Conference on Geographic Information Science, Florence, Italy, 5–9 July 2010.
- Hollenstein, L.; Purves, R. Exploring place through user-generated content: Using Flickr to describe city cores. J. Spat. Inf. Sci. 2010, 1, 21–48. [Google Scholar]
- Loconte, P.; Rotondo, F. VGI to enhance minor historic centres and their territorial cultural heritage. In Computational Science and Its Applications—ICCSA 2014; Springer: Berlin, Germany, 2014; pp. 315–329. [Google Scholar]
- Assessment of the Status of the Development of the Standards for the Terrestrial Essential Climate Variables. Available online: http://www.fao.org/gtos/doc/ECVs/T09/T09.pdf (accessed on 27 September 2016).
- Caetano, M.; Mata, F.; Freire, S. Accuracy assessment of the Portuguese CORINE Land Cover map. Glob. Dev. Environ. Earth Obs. Space 2006, 1, 459–467. [Google Scholar]
- Arsanjani, J.J.; Helbich, M.; Bakillah, M. Exploiting volunteered geographic information to ease land use mapping of an urban landscape. In Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, London, UK, 29–31 May 2013.
- Arsanjani, J.J.; Helbich, M.; Bakillah, M.; Hagenauer, J.; Zipf, A. Toward mapping land-use patterns from volunteered geographic information. Int. J. Geogr. Inf. Sci. 2013, 27, 2264–2278. [Google Scholar] [CrossRef]
- Estima, J.; Fonte, C.C.; Painho, M. Comparative study of Land Use/Cover classification using Flickr photos, satellite imagery and Corine Land Cover database. In Proceedings of the AGILE 2014 International Conference on Geographic Information Science, Castellón, Spain, 3–6 June 2014.
- Estima, J.; Painho, M. Flickr geotagged and publicly available photos: Preliminary study of its adequacy for helping quality control of corine land cover. Comput. Sci. Appl. 2013, 7974, 205–220. [Google Scholar]
- Estima, J.; Painho, M. Photo based volunteered geographic information initiatives. Int. J. Agric. Environ. Inf. Syst. 2014, 5, 73–89. [Google Scholar] [CrossRef]
- Estima, J.; Painho, M. Investigating the potential of OpenStreetMap for land use/land cover production: A case study for continental portugal. In OpenStreetMap in GIScience: Experiences, Research, Applications; Arsanjani, J.J., Zipf, A., Mooney, P., Helbich, M., Eds.; Springer: Berlin, Germany, 2015; pp. 273–293. [Google Scholar]
- Fonte, C.C.; Bastin, L.; See, L.; Foody, G.; Lupia, F. Usability of VGI for validation of land cover maps. Int. J. Geogr. Inf. Sci. 2015, 4, 1–23. [Google Scholar] [CrossRef]
- Foody, G.M.; Boyd, D.S. Using volunteered data in land cover map validation: Mapping West African forests. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2013, 6, 1305–1312. [Google Scholar] [CrossRef]
- Foody, G.M. Assessing the accuracy of land cover change with imperfect ground reference data. Remote Sens. Environ. 2010, 14, 2271–2285. [Google Scholar] [CrossRef]
- Fritz, S.; McCallum, I.; Schill, C.; Perger, C.; See, L.; Schepaschenko, D.; Velde, M.V.D.; Kraxner, F.; Obersteiner, M. Geo-Wiki: An online platform for improving global land cover. Environ. Model. Softw. 2012, 31, 110–123. [Google Scholar] [CrossRef]
- Hagenauer, J.; Helbich, M. Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks. Int. J. Geogr. Inf. Sci. 2012, 26, 963–982. [Google Scholar] [CrossRef]
- Arsanjani, J.J.; Vaz, E. An assessment of a collaborative mapping approach for exploring land use patterns for several European metropolises. Int. J. Appl. Earth Obs. Geoinf. 2015, 35, 329–337. [Google Scholar] [CrossRef]
- Perger, C.; Fritz, S.; See, L.; Schill, C.; Velde, M.V.D.; Mccallum, I.; Obersteiner, M. A campaign to collect volunteered geographic information on land cover and human impact. In GI Forum 2012: Geovizualisation, Society and Learning; Herbert Wichmann Verlag: Berlin, Germany, 2012; pp. 83–91. [Google Scholar]
- Goodchild, M. Citizens as sensors: The world of volunteered geography. GeoJournal 2007, 69, 211–221. [Google Scholar] [CrossRef]
- Turner, A.J. Introduction to Neogeography; O’Reilly Media: Sebastopol, CA, USA, 2006. [Google Scholar]
- Hudson-Smith, A.; Batty, M.; Crooks, A.; Milton, R. Mapping for the masses: Accessing web 2.0 through crowdsourcing. Soc. Sci. Comput. Rev. 2009, 27, 524–538. [Google Scholar] [CrossRef]
- Elwood, S.; Goodchild, M.F.; Sui, D.Z. Researching volunteered geographic information: Spatial data, geographic research, and new social practice. Ann. Assoc. Am. Geogr. 2012, 102, 571–590. [Google Scholar] [CrossRef]
- Stefanidis, A.; Crooks, A.; Radzikowski, J. Harvesting ambient geospatial information from social media feeds. GeoJournal 2013, 78, 319–338. [Google Scholar] [CrossRef]
- Fischer, F. VGI as big data: A new but delicate geographic data-source. Geoinformatics 2012, 5, 46–47. [Google Scholar]
- Brando, C.; Bucher, B. Quality in user generated spatial content: A matter of specifications. In Proceedings of the 13th AGILE International Conference on Geographic Information Science, Guimarães, Portugal, 11–14 May 2010.
- VGI-Net: A Collaborative Research Project. Available online: http://vgi.spatial.ucsb.edu/ (accessed on 27 September 2016).
- Hull, R.; Zhou, G. A framework for supporting data integration using the materialized and virtual approaches. In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Montreal, QC, Canada, 4–6 June 1996.
- Wiederhold, G. Mediators in the architecture of future information systems. Comput. Long. Beach. Calif. 1992, 25, 38–49. [Google Scholar] [CrossRef]
- Neill, C.J.; Laplante, P.A. Requirements engineering: The state of the practice. IEEE Softw. 2003, 20, 39–45. [Google Scholar] [CrossRef]
- Sencha Ext JS (Version 4.2.2). Available online: https://www.sencha.com/products/extjs/#overview (accessed on 27 September 2016).
- OpenLayers (Version 3.1.1). Available online: http://openlayers.org/ (accessed on 27 September 2016).
- Apache HTTP Server Project. Available online: https://httpd.apache.org/ (accessed on 27 September 2016).
- Brovelli, M.A.; Minghini, M.; Zamboni, G. Public participation GIS: A FOSS architecture enabling field-data collection. Int. J. Digit. Earth 2014, 7, 1–19. [Google Scholar]
- Horanont, T.; Basa, M.; Shibasaki, R. Towards thematic Web services for generic data visualization amd analysis. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2012, I-4, 147–150. [Google Scholar] [CrossRef]
- Cozannet, G.L.; Bagni, M.; Thierry, P.; Aragno, C.; Kouokam, E. WebGIS as boundary tools between scientific geoinformation and disaster risk reduction action in volcanic areas. Nat. Hazards Earth Syst. Sci. 2014, 14, 1591–1598. [Google Scholar] [CrossRef][Green Version]
- Okladnikov, I.; Gordov, E.; Titov, A.; Bogomolov, V.; Martynova, Y. Application of web-GIS approach for climate change study. EGU Gen. Assem. 2013, 15, 6682–6692. [Google Scholar]
- Simeoni, L.; Zatelli, P.; Floretta, C. Field measurements in river embankments: Validation and management with spatial database and webGIS. Nat. Hazard. 2014, 71, 1453–1473. [Google Scholar] [CrossRef]
- Burdziej, J. A Web-based spatial decision support system for accessibility analysis-concepts and methods. Appl. Geomat. 2012, 4, 75–84. [Google Scholar] [CrossRef]
- Haklay, M. How good is Volunteered Geographical Information? A comparative study of OpenStreetMap and Ordnance Survey datasets. Environ. Plan. B Plan. Des. 2008, 37, 682–703. [Google Scholar] [CrossRef]
- Fonte, C.C.; Bastin, L.; Foody, G.; Kellenberger, T.; Kerle, N.; Mooney, P.; Olteanu-Raimond, A.M.; See, L. VGI quality control. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2015, 3, 317–324. [Google Scholar] [CrossRef]
- Ma, D.; Sandberg, M.; Jiang, B. Characterizing the heterogeneity of the OpenStreetMap data and community. ISPRS Int. J. Geo-Inf. 2015, 4, 535–550. [Google Scholar] [CrossRef]
- Neis, P.; Zielstra, D. Recent developments and future trends in volunteered geographic information research: The case of OpenStreetMap. Future Int. 2014, 6, 76–106. [Google Scholar] [CrossRef]
- Antoniou, V.; Skopeliti, A. Measures and indicators of VGI quality: An overview. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2015, 3, 345–351. [Google Scholar] [CrossRef]
|Type of Requirement||Requirement|
|Spatial context||Data have to be georeferenced by coordinates|
|Spatial phenomena||Data have to represent, at least partially, physical aspects of the Earth|
|Data type||Photos and geometries are preferred but text can also be valuable if text mining tools are available and implemented|
|Access type||Data must be publically accessible through the Internet using open protocols|
|Data license||Data must be available free of charge for the purpose of land use/cover classification|
|Coverage||Depends on the type of coverage of the Land Use/Cover (LULC) being produced and the area of the Earth being classified|
|Name||Since||Spatial Context |
(Data Georeferenced by Coordinates)
|Spatial Phenomena||Coverage||Data Type||Access type||Availability|
© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).