Centaur VGI: A Hybrid Human–Machine Approach to Address Global Inequalities in Map Coverage

References

• Akerman, J. R. 2017. Decolonizing the map: Cartography from colony to nation. Chicago: University of Chicago Press.
   Google Scholar
• Anderson, J., D. Sarkar, and L. Palen. 2019. Corporate editors in the evolving landscape of OpenStreetMap. ISPRS International Journal of Geo-Information 8 (5):232. doi: 10.3390/ijgi8050232.
   Web of Science ®Google Scholar
• Baariu, S. N., G. C. Mulaku, and D. N. Siriba. 2019. State of cartographic services among the East African community member states. Journal of Geographic Information Systems 11 (1):56–65. doi: 10.4236/jgis.2019.111005.
   Google Scholar
• Bernard, J., M. Hutter, M. Zeppelzauer, D. Fellner, and M. Sedlmair. 2018. Comparing visual-interactive labeling with active learning: An experimental study. IEEE Transactions on Visualization and Computer Graphics 24 (1):298–308. doi: 10.1109/TVCG.2017.2744818.
   Web of Science ®Google Scholar
• Böhme, R. 1991. Inventory of world topographic mapping. Vol. 2: South America, Central America and Africa. Amsterdam: Elsevier Science.
   Google Scholar
• Chen, J., Y. Zhou, A. Zipf, and H. Fan. 2019. Deep learning from multiple crowds: A case study of humanitarian mapping. IEEE Transactions on Geoscience and Remote Sensing 57 (3):1713–22. doi: 10.1109/TGRS.2018.2868748.
   Web of Science ®Google Scholar
• Chen, J., and A. Zipf. 2017. DeepVGI: Deep learning with volunteered geographic information. In Proceedings of the 26th International Conference on World Wide Web Companion, ed. R. Barrett and R. Cummings, 771–72. Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee.
   Google Scholar
• Chinn, M. D., and R. W. Fairlie. 2006. The determinants of the global digital divide: A cross-country analysis of computer and internet penetration. Oxford Economic Papers 59 (1):16–44. doi: 10.1093/oep/gpl024.
   Web of Science ®Google Scholar
• Coleman, D. J. 2013. Potential contributions and challenges of VGI for conventional topographic base-mapping programs. In Crowdsourcing geographic knowledge, 245–63. New York: Springer.
   Google Scholar
• Coleman, D., Y. Georgiadou, and J. Labonte. 2009. Volunteered geographic information: The nature and motivation of produsers. International Journal of Spatial Data Infrastructures Research 4:332–58.
   Google Scholar
• Craglia, M., F. Ostermann, and L. Spinsanti. 2012. Digital Earth from vision to practice: Making sense of citizen-generated content. International Journal of Digital Earth 5 (5):398–416. doi: 10.1080/17538947.2012.712273.
   Web of Science ®Google Scholar
• Crutcher, M., and M. Zook. 2009. Placemarks and waterlines: Racialized cyberscapes in post-Katrina Google Earth. Geoforum 40 (4):523–34. doi: 10.1016/j.geoforum.2009.01.003.
   Web of Science ®Google Scholar
• Development Seed. 2019. Skynet—Machine learning for satellite imagery. Accessed January 16, 2019. https://developmentseed.org/projects/skynet/.
   Google Scholar
• Directorate of Overseas Surveys (1968). Sheet 23/2 (Pajule). Southampton, Ordnance Survey.
   Google Scholar
• Disaster Ninja. 2019. Disaster Ninja. Accessed October 15, 2019. http://disaster.ninja.
   Google Scholar
• Domingues, P. 2017. The master algorithm: How the quest for the ultimate learning machine will remake our world. London: Penguin.
   Google Scholar
• Du, B., Z. Wang, L. Zhang, L. Zhang, W. Liu, J. Shen, and D. Tao. 2017. Exploring representativeness and informativeness for active learning. IEEE Transactions on Cybernetics 47 (1):14–26. doi: 10.1109/TCYB.2015.2496974.
   Web of Science ®Google Scholar
• Elwood, S. 2008. Volunteered geographic information: Future research directions motivated by critical, participatory, and feminist GIS. GeoJournal 72 (3–4):173–83. doi: 10.1007/s10708-008-9186-0.
   Google Scholar
• Elwood, S. 2010. Geographic information science: Emerging research on the societal implications of the geospatial web. Progress in Human Geography 34 (3):349–57. doi: 10.1177/0309132509340711.
   Web of Science ®Google Scholar
• Elwood, S., M. F. Goodchild, and D. Z. Sui. 2012. Researching volunteered geographic information: Spatial data, geographic research, and new social practice. Annals of the Association of American Geographers 102 (3):571–90. doi: 10.1080/00045608.2011.595657.
   Web of Science ®Google Scholar
• Flanagin, A. J., and M. J. Metzger. 2008. The credibility of volunteered geographic information. GeoJournal 72 (3–4):137–48. doi: 10.1007/s10708-008-9188-y.
   Google Scholar
• Gartner, G., D. A. Bennett, and T. Morita. 2007. Towards ubiquitous cartography. Cartography and Geographic Information Science 34 (4):247–57. doi: 10.1559/152304007782382963.
   Google Scholar
• Gillies, M., R. Fiebrink, A. Tanaka, J. Garcia, F. Bevilacqua, A. Heloir, F. Nunnari, W. Mackay, S. Amershi, and B. Lee. 2016. Human-centred machine learning. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, ed. J. Kaye and A. Druin, 3558–65. New York: ACM. doi: 10.1145/2851581.2856492.
   Google Scholar
• Goodchild, M. F. 2007. Citizens as sensors: The world of volunteered geography. GeoJournal 69 (4):211–21. doi: 10.1007/s10708-007-9111-y.
   Google Scholar
• Goodchild, M. F., and L. Li. 2012. Assuring the quality of volunteered geographic information. Spatial Statistics 1:110–20. doi: 10.1016/j.spasta.2012.03.002.
   Web of Science ®Google Scholar
• Google. 2019. Tensorflow. Accessed October 15, 2019. https://www.tensorflow.org/about/.
   Google Scholar
• Graham, M., and M. Zook. 2011. Visualizing global cyberscapes: Mapping user-generated placemarks. Journal of Urban Technology 18 (1):115–32. doi: 10.1080/10630732.2011.578412.
   Web of Science ®Google Scholar
• Granell, C., and F. O. Ostermann. 2016. Beyond data collection: Objectives and methods of research using VGI and geo-social media for disaster management. Computers, Environment and Urban Systems 59:231–43. doi: 10.1016/j.compenvurbsys.2016.01.006.
   Web of Science ®Google Scholar
• Hagen, E. 2010. Putting Nairobi’s slums on the map. Development Outreach 12 (1):41–43. doi: 10.1596/1020-797X_12_1_41.
   Google Scholar
• Hagenauer, J., and M. Helbich. 2012. Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks. International Journal of Geographical Information Science 26 (6):963–82. doi: 10.1080/13658816.2011.619501.
   Web of Science ®Google Scholar
• Haklay, M. (2013). Citizen science and volunteered geographic information: Overview and typology of participation. Crowdsourcing geographic knowledge, Springer: 105–122.
   Google Scholar
• Haklay, M. 2010. How good is volunteered geographical information? A comparative study of OpenStreetMap and Ordnance Survey datasets. Environment and Planning B: Planning and Design 37 (4):315–322. doi: 10.1068/b35097.
   Google Scholar
• Haworth, B. T. 2016. Emergency management perspectives on volunteered geographic information: Opportunities, challenges and change. Computers, Environment and Urban Systems 57:189–98. doi: 10.1016/j.compenvurbsys.2016.02.009.
   Web of Science ®Google Scholar
• Haworth, B. T. 2018. Implications of volunteered geographic information for disaster management and GIScience: A more complex world of volunteered geography. Annals of the American Association of Geographers 108 (1):226–40. doi: 10.1080/24694452.2017.1321979.
   Web of Science ®Google Scholar
• Haworth, B. T., and E. Bruce. 2015. A review of volunteered geographic information for disaster management. Geography Compass 9 (5):237–50. doi: 10.1111/gec3.12213.
   Web of Science ®Google Scholar
• Herfort, B., M. Eckle, and J. P. de Albuquerque. 2016. Being specific about geographic information crowdsourcing: A typology and analysis of the Missing Maps Project in South Kivu. In Proceedings of the ISCRAM 2016 Conference, ed. A. Tapia, P. Antunes, V. A. Bañuls, K. Moore, and J. Porto. Rio de Janeiro, Brazil: Federal University of Rio de Janeiro.
   Google Scholar
• Herfort, B., H. Li, S. Fendrich, S. Lautenbach, and A. Zipf. 2019. Mapping human settlements with higher accuracy and less volunteer efforts by combining crowdsourcing and deep learning. Remote Sensing 11 (15):1799. doi: 10.3390/rs11151799.
   Web of Science ®Google Scholar
• Herfort, B., and A. Zipf. 2018. Enhancing crowdsourced classification on human settlements utilizing logistic regression aggregation and intrinsic context factors. In Geospatial technologies for all: Short papers, posters and poster abstracts of the 21th AGILE Conference on Geographic Information Science, ed. A. Mansourian, P. Pilesjö, L. Harrie, and R. von Lammeren. Lund University, Lund, Sweden, June 12-15. https://agile-online.org/conference/proceedings/proceedings-2018
   Google Scholar
• Holzinger, A. 2016. Interactive machine learning for health informatics: When do we need the human-in-the-loop? Brain Informatics 3 (2):119–31. doi: 10.1007/s40708-016-0042-6.
   PubMedGoogle Scholar
• Huck, J. (2018). Uganda. http://huckg.is/uganda50k. Accessed June 15, 2020.
   Google Scholar
• Huckathon. 2019. Community mapping Uganda. Accessed October 15, 2019. http://huckathon.org.
   Google Scholar
• Humanitarian OpenStreetMap Team. 2019. Tasking Manager. Accessed October 15, 2019. https://tasks.hotosm.org/contribute.
   Google Scholar
• Jordan, M. I., and T. M. Mitchell. 2015. Machine learning: Trends, perspectives, and prospects. Science 349 (6245):255–60. doi: 10.1126/science.aaa8415.
   PubMed Web of Science ®Google Scholar
• JOSM. 2019. JOSM. Accessed October 15, 2019. https://josm.openstreetmap.de.
   Google Scholar
• Konecny, G., U. Breitkopf, and A. Radtke. 2016. The status of topographic mapping in the world: A UNGGIM-ISPRS project 2012–2015. ISPRS—International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4 (2016). doi: 10.5194/isprsarchives-XLI-B4-737-2016.
   Google Scholar
• Lamwaka. 2016. The raging storm: A reporter’s inside account of the Northern Uganda War 1986–2005. Kampala, Uganda: Fountain.
   Google Scholar
• Li, H., B. Herfort, and A. Zipf. 2019. Estimating OpenStreetMap missing built-up areas using pre-trained deep neural networks. In Geospatial technologies for local and regional development: Short papers, posters and poster abstracts of the 22nd AGILE Conference on Geographic Information Science, ed. P. Kyriakidis, D. Hadjimitsis, D. Skarlatos, and A. Mansourian. Cyprus University of Technology, Limassol, Cyprus, June 17-20. https://agile-online.org/conference/proceedings.
   Google Scholar
• Liu, P., H. Zhang, and K. B. Eom. 2017. Active deep learning for classification of hyperspectral images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10 (2):712–24. doi: 10.1109/JSTARS.2016.2598859.
   Web of Science ®Google Scholar
• Map with AI. 2019. Using AI to map the world. Accessed December 10, 2019. https://mapwith.ai.
   Google Scholar
• Natural Earth (2020). Natural Earth. https://www.naturalearthdata.com. Accessed June 15, 2020.
   Google Scholar
• Norris, J. 2015. Future trends in geospatial information management: The five to ten year vision. Southampton, UK: Ordnance Survey.
   Google Scholar
• Obermeyer, N. 2007. Thoughts on volunteered (geo)-slavery. In NCGIA and Vespucci workshop on volunteered geographical information, ed. M. F. Goodchild and R. Gupta, 13-14. Santa Barbara, CA: The National Center for Geographic Information and Analysis.
   Google Scholar
• Okello, T. R., S. M. Magada, P. Atim, D. Ezati, A. Campion, E. B. Moro, J. Huck, G. Byrne, A. Redmond, and M. Nirmalan. 2019. Major limb loss (MLL): An overview of etiology, outcomes, experiences and challenges faced by amputees and service providers in the post-conflict period in northern Uganda. Journal of Global Health Reports 3:1–10. doi: 10.29392/joghr.3.e2019028.
   Google Scholar
• Olteanu-Raimond, A. M., G. Hart, G. M. Foody, G. Touya, T. Kellenberger, and D. Demetriou. 2017. The scale of VGI in map production: A perspective on European national mapping agencies. Transactions in GIS 21 (1):74–90. doi: 10.1111/tgis.12189.
   Web of Science ®Google Scholar
• OpenStreetMap. 2019a. iD. Accessed October 15, 2019. https://wiki.openstreetmap.org/wiki/ID.
   Google Scholar
• OpenStreetMap. 2019b. Import guidelines. Accessed October 15, 2019. https://wiki.openstreetmap.org/wiki/Import/Guidelines.
   Google Scholar
• OpenStreetMap. 2019c. Imports [Mailing list] archives. Accessed October 15, 2019. https://lists.openstreetmap.org/pipermail/imports/.
   Google Scholar
• OpenStreetMap. 2019d. Imports slated for deletion. Accessed October 15, 2019. https://wiki.openstreetmap.org/wiki/Import/Catalogue#Imports_slated_for_deletion.
   Google Scholar
• OpenStreetMap. 2019e. What’s the problem with mechanical edits? Accessed October 15, 2019. https://wiki.openstreetmap.org/wiki/What%27s_the_problem_with_mechanical_edits%3F.
   Google Scholar
• OpenStreetMap Analytics. 2019. OpenStreetMap Analytics. Accessed October 15, 2019. https://osm-analytics.org/#/gaps.
   Google Scholar
• Parry, B., and C. Perkins. 2000. World mapping today. 2nd ed. Berlin: Walter de Gruyter.
   Google Scholar
• Patra, S., and L. Bruzzone. 2011. A fast cluster-assumption based active-learning technique for classification of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing 49 (5):1617–26. doi: 10.1109/TGRS.2010.2083673.
   Web of Science ®Google Scholar
• Perkins, C. 1987. Map acquisition. In World mapping today, ed. R. B. P. Parry and C. Perkins, 15–26. London: Butterworths.
   Google Scholar
• Perkins, C., and M. Dodge. 2009. Satellite imagery and the spectacle of secret spaces. Geoforum 40 (4):546–60. doi: 10.1016/j.geoforum.2009.04.012.
   Web of Science ®Google Scholar
• POSM. 2019. Portable OpenStreetMap. Accessed October 15, 2019. https://github.com/posm/posm.
   Google Scholar
• Radcliffe, S. A. 2009. National maps, digitalisation and neoliberal cartographies: Transforming nation‐state practices and symbols in postcolonial Ecuador. Transactions of the Institute of British Geographers 34 (4):426–44. doi: 10.1111/j.1475-5661.2009.00359.x.
   Web of Science ®Google Scholar
• Scott, G., and A. Rajabifard. 2017. Sustainable development and geospatial information: A strategic framework for integrating a global policy agenda into national geospatial capabilities. Geo-Spatial Information Science 20 (2):59–76. doi: 10.1080/10095020.2017.1325594.
   Web of Science ®Google Scholar
• Senaratne, H., A. Mobasheri, A. L. Ali, C. Capineri, and M. Haklay. 2017. A review of volunteered geographic information quality assessment methods. International Journal of Geographical Information Science 31 (1):139–67. doi: 10.1080/13658816.2016.1189556.
   Web of Science ®Google Scholar
• Settles, B. 2009. Active learning literature survey. Madison: University of Wisconsin–Madison Department of Computer Sciences.
   Google Scholar
• Sharma, M., and M. Bilgic. 2017. Evidence-based uncertainty sampling for active learning. Data Mining and Knowledge Discovery 31 (1):164–202. doi: 10.1007/s10618-016-0460-3.
   Web of Science ®Google Scholar
• Smith, N. 2018. Integrating machine learning into the Tasking Manager: Notes on a direction. Washington, DC: Humanitarian OpenStreetMap Team.
   Google Scholar
• Stumpf, A., N. Lachiche, J.-P. Malet, N. Kerle, and A. Puissant. 2014. Active learning in the spatial domain for remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing 52 (5):2492–507. doi: 10.1109/TGRS.2013.2262052.
   Web of Science ®Google Scholar
• Sui, D., M. Goodchild, and S. Elwood. 2013. Volunteered geographic information, the exaflood, and the growing digital divide. In Crowdsourcing geographic knowledge, ed. D. Sui, M. Goodchild, and S. Elwood, 1–12. Berlin: Springer.
   Google Scholar
• Thompson, C. 2013. Smarter than you think: How technology is changing our minds for the better. London: William Collins.
   Google Scholar
• Vargas-Muñoz, J. E., S. Lobry, A. X. Falcão, and D. Tuia. 2019. Correcting rural building annotations in OpenStreetMap using convolutional neural networks. ISPRS Journal of Photogrammetry and Remote Sensing 147:283–93. doi: 10.1016/j.isprsjprs.2018.11.010.
   Web of Science ®Google Scholar
• Vinci, A. 2005. The strategic use of fear by the Lord’s Resistance Army. Small Wars & Insurgencies 16 (3):360–81. doi: 10.1080/09592310500221336.
   Google Scholar
• Yang, L., A. MacEachren, P. Mitra, and T. Onorati. 2018. Visually-enabled active deep learning for (geo) text and image classification: A review. ISPRS International Journal of Geo-Information 7 (2):65. doi: 10.3390/ijgi7020065.
   Web of Science ®Google Scholar
• Yuan, J., P. K. Roy Chowdhury, J. McKee, H. L. Yang, J. Weaver, and B. Bhaduri. 2018. Exploiting deep learning and volunteered geographic information for mapping buildings in Kano, Nigeria. Scientific Data 5:180217. doi: 10.1038/sdata.2018.217.
   Web of Science ®Google Scholar
• Zhu, X. X., D. Tuia, L. Mou, G.-S. Xia, L. Zhang, F. Xu, and F. Fraundorfer. 2017. Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine 5 (4):8–36. doi: 10.1109/MGRS.2017.2762307.
   Web of Science ®Google Scholar
• Zielstra, D., and A. Zipf. 2010. A comparative study of proprietary geodata and volunteered geographic information for Germany. Paper presented at 13th AGILE International Conference on Geographic Information Science, Guimarães, Portugal, May 11-14.
   Google Scholar
• Zook, M., M. Graham, T. Shelton, and S. Gorman. 2010. Volunteered geographic information and crowdsourcing disaster relief: A case study of the Haitian earthquake. World Medical & Health Policy 2 (2):6–33. doi: 10.2202/1948-4682.1069.
   Google Scholar