References
- 1Amano, T, Lamming, JD and Sutherland, WJ. 2016. Spatial gaps in global biodiversity information and the role of citizen science. Bioscience, 66(5): 393–400. DOI: 10.1093/biosci/biw022
- 2Anderson, K, Ryan, B, Sonntag, W, Kavvada, A and Friedl, L. 2017. Earth observation in service of the 2030 Agenda for Sustainable Development. Geo-spatial Information Science, 20(2): 77–96. DOI: 10.1080/10095020.2017.1333230
- 3Andries, A, Morse, S, Murphy, RJ, Sadhukhan, J, Martinez-Hernandez, E, Amezcua-Allieri, MA and Aburto, J. 2023. Potential of Using Night-Time Light to Proxy Social Indicators for Sustainable Development. Remote Sensing, 15(5): 1209. DOI: 10.3390/rs15051209
- 4Antoniou, V. 2011.
User generated spatial content: an analysis of the phenomenon and its challenges for mapping agencies . PhD Thesis, University College London. - 5Antoniou, V and Potsiou, C. 2020. A Deep Learning Method to Accelerate the Disaster Response Process. Remote Sensing, 12(3): 544. DOI: 10.3390/rs12030544
- 6Apostolakis, A, Girtsou, S, Giannopoulos, G, Bartsotas, NS and Kontoes, C. 2022. Estimating Next Day’s Forest Fire Risk via a Complete Machine Learning Methodology. Remote Sensing, 14(5): 1222. DOI: 10.3390/rs14051222
- 7Apostolopoulos, K and Potsiou, C. 2022. Consideration on how to introduce gamification tools to enhance citizen engagement in crowdsourced cadastral surveys. Survey Review, 54(383): 142–152. DOI: 10.1080/00396265.2021.1888027
- 8Basiouka, S, Potsiou, C and Bakogiannis, E. 2015. OpenStreetMap for cadastral purposes: an application using VGI for official processes in urban areas. Survey Review, 47(344): 333–341. DOI: 10.1179/1752270615Y.0000000011
- 9Bradter, U, Mair, L, Jönsson, M, Knape, J, Singer, A and Snäll, T. 2018. Can opportunistically collected Citizen Science data fill a data gap for habitat suitability models of less common species? Methods in Ecology and Evolution, 9(7): 1667–1678. DOI: 10.1111/2041-210X.13012
- 10Carter, WN. 1991. Disaster Management: A Disaster Manager’s Handbook. Manila: Mamta Publication.
- 11Castell, N, Dauge, FR, Schneider, P, Vogt, M, Lerner, U, Fishbain, B, Broday, D and Bartonova, A. 2017. Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates? Environment international, 99: 293–302. DOI: 10.1016/j.envint.2016.12.007
- 12Ceccaroni, L, Bibby, J, Roger, E, Flemons, P, Michael, K, Fagan, L and Oliver, JL. 2019. Opportunities and risks for citizen science in the age of artificial intelligence. Citizen Science: Theory and Practice, 4(1). DOI: 10.5334/cstp.241
- 13Challies, E, Newig, J, Thaler, T, Kochskämper, E and Levin-Keitel, M. 2016. Participatory and collaborative governance for sustainable flood risk management: An emerging research agenda. Environmental Science and Policy, 55(2016): 275–280. DOI: 10.1016/j.envsci.2015.09.012
- 14Chatenoux, B, et al. 2021. The Swiss data cube, analysis ready data archive using earth observations of Switzerland. Scientific data, 8: 1–11. DOI: 10.1038/s41597-021-01076-6
- 15Dalyot, K and Dalyot, S. 2018. Towards the use of crowdsourced GIS data to steferenced child well-being globally. Social Indicators Research, 139(1): 185–204. DOI: 10.1007/s11205-017-1714-1
- 16Eveleigh, A, Jennett, C, Lynn, S and Cox, AL. 2013. “I want to be a captain! I want to be a captain!” gamification in the old weather citizen science project. In: First international conference on gameful design, research, and applications, 79–82. Toronto, Canada on
October 2–4 . DOI: 10.1145/2583008.2583019 - 17Fonte, CC, Minghini, M, Patriarca, J, Antoniou, V, See, L and Skopeliti, A. 2017. Generating up-to-date and detailed land use and land cover maps using OpenStreetMap and GlobeLand30. ISPRS International Journal of Geo-Information, 6(4): 125. DOI: 10.3390/ijgi6040125
- 18Foody, GM, Ling, F, Boyd, DS, Li, X and Wardlaw, J. 2019. Earth observation and machine learning to meet Sustainable Development Goal 8.7: Mapping sites associated with slavery from space. Remote Sensing, 11(3): 266. DOI: 10.3390/rs11030266
- 19Fraisl, D, Campbell, J, See, L, Wehn, U, Wardlaw, J, Gold, M, Moorthy, I, Arias, R, Piera, J, Oliver, J, Masó, J, Penker, M and Fritz, S. 2020. Mapping citizen science contributions to the UN sustainable development goals. Sustainability Science, 15: 1735–1751. DOI: 10.1007/s11625-020-00833-7
- 20Fritz, S, See, L, Carlson, T, Haklay, MM, Oliver, JL, Fraisl, D, Mondardini, R, Brocklehurst, M, Shanley, LA, Schade, S and When, U. 2019. Citizen science and the United Nations Sustainable Development Goals. Nature Sustainability, 2(10): 922–930. DOI: 10.1038/s41893-019-0390-3
- 21Goodchild, MF. 2007. Citizens as sensors: the world of volunteered geography. GeoJournal, 69(4): 211–221. DOI: 10.1007/s10708-007-9111-y
- 22Haklay, M. 2013. Citizen science and volunteered geographic information: Overview and typology of participation. Crowdsourcing geographic knowledge, 105–122. DOI: 10.1007/978-94-007-4587-2_7
- 23Haklay, M, Antoniou, V, Basiouka, S, Soden, R and Mooney, P. 2014. Crowdsourced geographic information use in government. London: World Bank Publications.
- 24Haklay, M, Antoniou, V, Basiouka, S, Soden, RJ, Deparday, V, Sheely, RM and Mooney, P. 2018. Identifying Success Factors in Crowdsourced Geographic Information Use in Government. London: World Bank Publications.
- 25Hsu, A, Malik, O, Johnson, L and Esty, DC. 2014. Development: Mobilize citizens to track sustainability. Nature News, 508(7494): 33. DOI: 10.1038/508033a
- 26Irwin, A. 1995. Citizen science: A study of people, expertise and sustainable development. London: Routledge.
- 27Jean, N, Burke, M, Xie, M, Davis, WM, Lobell, DB and Ermon, S. 2016. Combining satellite imagery and machine learning to predict poverty. Science, 353(6301): 790–794. DOI: 10.1126/science.aaf789
- 28Karwowska, K and Wierzbicki, D. 2022. Using Super-Resolution Algorithms for Small Satellite Imagery: A Systematic Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. DOI: 10.1109/JSTARS.2022.3167646
- 29Kavvada, A, Metternicht, G, Kerblat, F, Mudau, N, Haldorson, M, Laldaparsad, S, Friedl, L, Held, A and Chuvieco, E. 2020. Towards delivering on the sustainable development goals using earth observations. Remote Sensing of Environment, 247:
111930 . DOI: 10.1016/j.rse.2020.111930 - 30Li, H, Zech, J, Ludwig, C, Fendrich, S, Shapiro, A, Schultz, M and Zipf, A. 2021. Automatic mapping of national surface water with OpenStreetMap and Sentinel-2 MSI data using deep learning. International Journal of Applied Earth Observation and Geoinformation, 104:
102571 . DOI: 10.1016/j.jag.2021.102571 - 31Mateo-Garcia, G, Veitch-Michaelis, J, Smith, L, Oprea, SV, Schumann, G, Gal, Y, Baydin, AG and Backes, D. 2021. Towards global flood mapping onboard low cost satellites with machine learning. Scientific reports, 11(1): 1–12. DOI: 10.1038/s41598-021-86650-z
- 32Maxwell, AE, Warner, TA and Fang, F. 2018. Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing, 39(9): 2784–2817. DOI: 10.1080/01431161.2018.1433343
- 33Moallemi, EA, Malekpour, S, Hadjikakou, M, Raven, R, Szetey, K, Ningrum, D, Dhiaulhaq, A and Bryan, BA. 2020. Achieving the sustainable development goals requires transdisciplinary innovation at the local scale. One Earth, 3(3): 300–313. DOI: 10.1016/j.oneear.2020.08.006
- 34Murray, V, Maini, R, Clarke, L and Eltinay, N. 2017. Coherence between the Sendai Framework, the SDGs, the Climate Agreement, New Urban Agenda and World Humanitarian Summit, and the role of science in their implementation. In: Global Platform for Disaster Risk Reduction, Cancun, Mexico on
22–26 May 2017 , 24–26. - 35Pánek, J, Marek, L, Pászto, V and Valůch, J. 2017. The Crisis Map of the Czech Republic: the nationwide deployment of an Ushahidi application for disasters. Disasters, 41(4): 649–671. DOI: 10.1111/disa.12221
- 36Park, S, Im, J, Jang, E and Rhee, J. 2016. Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions. Agricultural and forest meteorology, 216: 157–169. DOI: 10.1016/j.agrformet.2015.10.011
- 37Quinlivan, L, Chapman, DV and Sullivan, T. 2020. Applying citizen science to monitor for the Sustainable Development Goal Indicator 6.3.2: a review. Environmental monitoring and assessment, 192: 1–11. DOI: 10.1007/s10661-020-8193-6
- 38Rabiee, M. 2019.
Spatially Enabling the SDGs: The Social, Economic, and Environmental Impacts of Spatial Enablement . In: Sustainable Development Goals Connectivity Dilemma: Land and Geospatial Information for Urban and Rural Resilience, 65–77. Boca Raton: CRC Press. DOI: 10.1201/9780429290626-4 - 39Rahmatizadeh, S, Rajabifard, A and Kalantari, M. 2016. A conceptual framework for utilising VGI in land administration. Land Use Policy, 56: 81–89. DOI: 10.1016/j.landusepol.2016.04.027
- 40Rajabifard, A, Crompvoets, J, Kalantari, M and Kok, B. 2010. Spatially enabling society: research, emerging trends and critical assessment. Belgium: Leuven University Press.
- 41Rajabifard, A, Potts, K, Torhonen, MP, Barra, F and Justiniano, I. 2019.
Leveraging National Land and Geospatial Systems for Improved Disaster Resilience . In: Sustainable Development Goals Connectivity Dilemma: Land and Geospatial Information for Urban and Rural Resilience, 81–92. Boca Raton: CRC Press. DOI: 10.1201/9780429290626-5 - 42Roberts, E, Andrei, S, Huq, S and Flint, L. 2015. Resilience synergies in the post-2015 development agenda. Nature Climate Change, 5(12): 1024. DOI: 10.1038/nclimate2776
- 43Rönneberg, M and Kettunen, P. 2021. Enabling citizens to refine the location accuracy of cadastre boundary markers by gamified VGI. Abstracts of the ICA, 3: 252. DOI: 10.5194/ica-abs-3-252-2021
- 44Rostow, WW and Rostow, WW. 1990.
The stages of economic growth: A non-communist manifesto . Cambridge, UK: Cambridge University Press. DOI: 10.1017/CBO9780511625824 - 45Savan, B, Morgan, AJ and Gore, C. 2003. Volunteer environmental monitoring and the role of the universities: the case of Citizens’ Environment Watch. Environmental management, 31(5): 0561–0568. DOI: 10.1007/s00267-002-2897-y
- 46Scott, G and Rajabifard, A. 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
- 47Scott, G and Rajabifard, A. 2019.
SDGs Roadmap . In: Sustainable Development Goals Connectivity Dilemma: Land and Geospatial Information for Urban and Rural Resilience, 10–34. Boca Raton: CRC Press. DOI: 10.1201/9780429290626 - 48Sheppard, SA and Terveen, L. 2011. Quality is a verb: the operationalization of data quality in a citizen science community. In: 7th International Symposium on Wikis and Open Collaboration, 29–38. Mountain View, CA on
3–5 October 2011 . DOI: 10.1145/2038558.2038565 - 49Silvertown, J. 2009. A new dawn for citizen science. Trends in ecology & evolution, 24(9): 467–471. DOI: 10.1016/j.tree.2009.03.017
- 50Sosko, S and Dalyot, S. 2017. Crowdsourcing user-generated mobile sensor weather data for densifying static geosensor networks. ISPRS International Journal of Geo-Information, 6(3): 61. DOI: 10.3390/ijgi6030061
- 51Stehman, SV, Fonte, CC, Foody, GM and See, L. 2018. Using volunteered geographic information (VGI) in design-based statistical inference for area estimation and accuracy assessment of land cover. Remote Sensing of Environment, 212: 47–59. DOI: 10.1016/j.rse.2018.04.014
- 52Sultan, J, Ben-Haim, G, Haunert, JH and Dalyot, S. 2017. Extracting spatial patterns in bicycle routes from crowdsourced data. Transactions in GIS, 21(6): 1321–1340. DOI: 10.1111/tgis.12280
- 53Tilon, S, Nex, F, Kerle, N and Vosselman, G. 2020. Post-disaster building damage detection from earth observation imagery using unsupervised and transferable anomaly detecting generative adversarial networks. Remote sensing, 12(24): 4193. DOI: 10.3390/rs12244193
- 54Tomaszewski, B, Judex, M, Szarzynski, J, Radestock, C and Wirkus, L. 2015. Geographic information systems for disaster response: A review. Journal of Homeland Security and Emergency Management, 12(3): 571–602. DOI: 10.1515/jhsem-2014-0082
- 55UN. 2015a. Transforming our world: The 2030 agenda for sustainable development. General Assembley 70 session. [online access at:
https://documents-dds-ny.un.org/doc/UNDOC/GEN/N15/291/89/PDF/N1529189.pdf?OpenElement last accessed 28 September 2022]. - 56UN. 2015b. Sendai framework for disaster risk reduction 2015–2030. [online access at:
https://www.preventionweb.net/files/43291_sendaiframeworkfordrren.pdf last accessed 28 September 2022]. - 57UN. 2015c. Earth Observations in Support of National Strategies for Disaster-Risk Management [available online at:
http://www.un-spider.org/sites/default/files/White_paper_Earth%20observations%20for%20DRR.pdf last accessed 28 September 2022]. - 58UN. 2017. Earth Observations for Official Statistics [online available at:
https://unstats.un.org/bigdata/taskteams/satellite/UNGWG_Satellite_Task_Team_Report_WhiteCover.pdf last accessed 28 September 2022]. - 59UN. 2022. The Sustainable Development Goals Report [online available at:
https://unstats.un.org/sdgs/report/2022/The-Sustainable-Development-Goals-Report-2022.pdf last accessed 28 March 2023]. - 60UN.
Australian Bureau of Statistics; Queensland University of Technology; Queensland Government; Commonwealth Scientific and Industrial Research Organisation; European Commission; National Institute of Statistics and Geography; Statistics Canada . Earth Observations for Official Statistics, 2017. [online available at:https://unstats.un.org/bigdata/task-teams/earth-observation/UNGWG_Satellite_Task_Team_Report_WhiteCover.pdf last accessed 28 March 2023]. - 61UNDRR. 2016. Implementing the Sendai Framework to achieve the Sustainable Development Goals. [online access at:
https://www.unisdr.org/files/50438_implementingthesendaiframeworktoach.pdf last accessed 28 September 2022]. - 62UN-GGIM and World Bank. 2018. Integrated geospatial information framework a strategic guide to develop and strengthen national geospatial information management. [online access at:
http://ggim.un.org/meetings/GGIM-committee/8th-Session/documents/Part%201-IGIF-Overarching-Strategic-Framework-24July2018.pdf last accessed 28 September 2022]. - 63UNISDR. 2005. Hyogo framework for action 2005–2015 [online access at:
https://www.unisdr.org/2005/wcdr/intergover/official-doc/L-docs/Hyogo-framework-for-action-english.pdf last accessed 28 September 2022]. - 64UNTT. 2015. Working Group on Sustainable Development Financing [online access at:
https://sustainabledevelopment.un.org/content/documents/2096Chapter%201-global%20investment%20requirement%20estimates.pdf last accessed 28 September 2022]. - 65United States Department of Homeland Security. 2013. National Response Framework. 2nd ed. [online access at:
https://www.hsdl.org/?view&did=735934 last accessed 28 September 2022]. - 66Van den Homberg, M and Susha, I. 2018. Characterizing data ecosystems to support official statistics with open mapping data for reporting on sustainable development goals. ISPRS International Journal of Geo-Information, 7(12): 456. DOI: 10.3390/ijgi7120456
- 67Vinuesa, R, Azizpour, H, Leite, I, Balaam, M, Dignum, V, Domisch, S, Felländer, A, Langhans, SD, Tegmark, M and Nerini, FF. 2020. The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1): 1–10. DOI: 10.1038/s41467-019-14108-y
- 68Vohland, K, Sauermann, H, Antoniou, V, Balazs, B, Göbel, C, Karatzas, K, Mooney, P, Perelló, J, Ponti, M, Samson, R and Winter, S. 2019. Citizen science and sustainability transitions. Research Policy, 49(5):
103978 . DOI: 10.1016/j.respol.2020.103978 - 69Vorisek, D and Yu, S. 2020. Understanding the cost of achieving the Sustainable Development Goals. [online access at:
https://openknowledge.worldbank.org/server/api/core/bitstreams/06eb043e-3b06-5547-bfa4-56f4c1202c23/content last accessed 24 March 2023]. DOI: 10.1596/1813-9450-9164 - 70Wallace, J, Williamson, IP, Rajabifard, A and Bennett, R. 2006. Spatial information opportunities for Government. Journal of Spatial Science, 51(1): 79–99. DOI: 10.1080/14498596.2006.9635066
- 71Wegner, JD, Roscher, R, Volpi, M and Veronesi, F. 2018. Foreword to the Special Issue on Machine Learning for Geospatial Data Analysis. ISPRS International Journal of Geo-Information, 7(4): 147. DOI: 10.3390/ijgi7040147
- 72Williamson, I, Rajabifard, A and Wallace, J. 2007. Spatially Enabling Government–An International Challenge. In: International Workshop on Spatial Enablement of Government and NSDI–Policy Implications, Seoul, Korea on
12 June 2007 , pp. 1–12. - 73World Bank & FAO. 2011. Global Strategy to Improve Agricultural and Rural Statistics [online access at:
http://www.fao.org/fileadmin/templates/ess/documents/meetings_and_workshops/ICAS5/Ag_Statistics_Strategy_Final.pdf last accessed 28 September 2022]. - 74Wu, B, Tian, F, Zhang, M, Zeng, H and Zeng, Y. 2020. Cloud services with big data provide a solution for monitoring and tracking sustainable development goals. Geography and Sustainability, 1(1): 25–32. DOI: 10.1016/j.geosus.2020.03.006
- 75Yuan, Q, Shen, H, Li, T, Li, Z, Li, S, Jiang, Y, Xu, H, Tan, W, Yang, Q, Wang, J, Gao, J and Zhang, L. 2020. Deep learning in environmental remote sensing: Achievements and challenges. Remote Sensing of Environment, 241:
111716 . DOI: 10.1016/j.rse.2020.111716 - 76Zennaro, F, Furlan, E, Simeoni, C, Torresan, S, Aslan, S, Critto, A and Marcomini, A. 2021. Exploring machine learning potential for climate change risk assessment. Earth-Science Reviews, 220:
103752 . DOI: 10.1016/j.earscirev.2021.103752 - 77Zook, M, Graham, M, Shelton, T and Gorman, S. 2010. Volunteered geographic information and crowdsourcing disaster relief: a case study of the Haitian earthquake. World Medical & Health Policy, 2(2): 7–33. DOI: 10.2202/1948-4682.1069
