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Research Articles

A structural catalogue of the settlement morphology in refugee and IDP camps

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 1338-1364 | Received 05 Sep 2022, Accepted 07 Mar 2023, Published online: 27 Mar 2023

References

  • Ahimbisibwe, F., 2019. Uganda and the refugee problem: challenges and opportunities. African Journal of Political Science and International Relations, 13 (5), 62–72.
  • Aravena Pelizari, P., et al., 2018. Multi-sensor feature fusion for very high spatial resolution built-up area extraction in temporary settlements. Remote Sensing of Environment, 209, 793–807.
  • Aylett-Bullock, J., et al., 2021. Operational response simulation tool for epidemics within refugee and IDP settlements: A scenario-based case study of the cox’s bazar settlement. PLoS Computational Biology, 17 (10), e1009360.
  • Brant, R., 1990. Assessing proportionality in the proportional odds model for ordinal logistic regression. Biometrics, 46 (4), 1171–1178.
  • Braun, A., Fakhri, F., and Hochschild, V., 2019. Refugee camp monitoring and environmental change assessment of Kutupalong, Bangladesh, based on radar imagery of sentinel-1 and ALOS-2. Remote Sensing, 11 (17), 2047.
  • Chan, C.Y.C., et al., 2022. Investigating the capability of uav imagery for ai-assisted mapping of refugee camps in east africa. In: M. Minghini, P. Liu, H. Li, A.Y. Grindberger and L. Juhász, eds. Proceedings of the Academic Track at State of the Map 2022, Florence, Italy: Zenodo, 45–48.
  • Cornebise, J., Oršolić, I., and Kalaitzis, F., 2022. Open high-resolution satellite imagery: The worldstrat dataset – with application to super-resolution.
  • Freeman, H., and Shapira, R., 1975. Determining the minimum-area encasing rectangle for an arbitrary closed curve. Communications of the ACM, 18 (7), 409–413.
  • Georganos, S., et al., 2021. Is it all the same? mapping and characterizing deprived urban areas using WorldView-3 superspectral imagery. a case study in Nairobi, Kenya. Remote Sensing, 13 (24), 4986.
  • Grilli, L., and Rampichini, C., 2021. Ordered logit model. In: F. Maggino, ed. Encyclopedia of quality of life and well-being research. Cham: Springer International Publishing, 1–4. https://doi.org/10.1007/978-3-319-69909-7_2023-2
  • Inter-Agency Shelter Sector Corrdination Working Group 2018., Guidelines for the fire prevention, preparedness, and response (fppr). Temporary Technical Committee Led & Developed by Save the Children International (SCI). Available from: https://data2.unhcr.org/en/documents/download/62513.
  • International Organization for Migration 2021., Environmental migration disaster displacement and planned relocation in west aftrica [IOM – International Organization for Migration, Geneva, Switzerland]. Geneva, Switzerland: International Organization for Migration (IOM). Available from: https://publications.iom.int/system/files/pdf/Environmental-Migration-Disaster-Displacement-in-West-Africa.pdf.
  • Jochem, W.C., et al., 2021. Classifying settlement types from multi-scale spatial patterns of building footprints. Environment and Planning B: Urban Analytics and City Science, 48 (5), 1161–1179.
  • Jochem, W.C., and Tatem, A.J., 2021. Tools for mapping multi-scale settlement patterns of building footprints: An introduction to the r package foot. PLoS One, 16 (2), e0247535.
  • Kraff, N.J., Wurm, M., and Taubenböck, H., 2020a. The dynamics of poor urban areas - analyzing morphologic transformations across the globe using earth observation data. Cities, 107, 102905.
  • Kraff, N.J., Wurm, M., and Taubenböck, H., 2022. Housing forms of poverty in europe - a categorization based on literature research and satellite imagery. Applied Geography, 149, 102820.
  • Kraff, N.J., Wurm, M., and Taubenböck, H., 2020b. Uncertainties of human perception in visual image interpretation in complex urban environments. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 4229–4241.
  • Kuffer, M., Barros, J., and Sliuzas, R.V., 2014. The development of a morphological unplanned settlement index using very-high-resolution (VHR) imagery. Computers, Environment and Urban Systems, 48, 138–152.
  • Lang, S., et al., 2020. Earth observation tools and services to increase the effectiveness of humanitarian assistance. European Journal of Remote Sensing, 53 (sup2), 67–85.
  • Lang, S., et al., 2010. Earth observation (EO)-based ex post assessment of internally displaced person (IDP) camp evolution and population dynamics in zam zam, darfur. International Journal of Remote Sensing, 31 (21), 5709–5731.
  • McAuliffe, M., Lee, T., and Abel, G., 2021. Migration and migrants: A global overview. In: M. McAuliffe and A. Triandafyllidou, eds. World migration report 2022. Geneva: International Organization for Migration (IOM), Ch. 2, 21–57.
  • McAuliffe, M., and Triandafyllidou, A., 2021. Report overview: Technological, geopolitical and environmental transformations shaping our migration andmobility futures. In: M. McAuliffe and A. Triandafyllidou, eds. World migration report 2022. Geneva: International Organization for Migration (IOM), Ch. 1, 1–17.
  • Nasir, M., et al., 2022. Dwelling type classification for disaster risk assessment using satellite imagery.
  • Quinn, J.A., et al., 2018. Humanitarian applications of machine learning with remote-sensing data: review and case study in refugee settlement mapping. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376 (2128), 20170363.
  • Ramadan, A., 2013. Spatialising the refugee camp. Transactions of the Institute of British Geographers, 38 (1), 65–77.
  • Sirko, W., et al., 2021. Continental-scale building detection from high resolution satellite imagery. online, https://arxiv.org/pdf/2107.12283v2.pdf, July. Available from: https://arxiv.org/pdf/2107.12283v2.pdf.
  • Sphere Assotication 2018., ed., The sphere handbook: Humanitarian charter and minimum standards in humanitarian response. 4th ed. Geneva, Switzerland: Practical Action Publishing.
  • Stark, T., et al., 2020. Satellite-based mapping of urban poverty with transfer-learned slum morphologies. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5251–5263.
  • Taubenböck, H., and Kraff, N.J., 2014. The physical face of slums: a structural comparison of slums in mumbai, india, based on remotely sensed data. Journal of Housing and the Built Environment, 29 (1), 15–38.
  • Taubenböck, H., Kraff, N., and Wurm, M., 2018. The morphology of the arrival city - a global categorization based on literature surveys and remotely sensed data. Applied Geography, 92, 150–167.
  • Taubenböck, H., et al., 2019. A new ranking of the world’s largest cities—do administrative units obscure morphological realities? Remote Sensing of Environment, 232, 111353.
  • Tomaszewski, B., et al., 2016. Infrastructure evolution analysis via remote sensing in an urban refugee camp – evidence from Za’atari. Procedia Engineering, 159, 118–123.
  • UNHCR 2021a., Global report 2020. online Available from: https://reporting.unhcr.org/sites/default/files/gr2020/pdf/GR2020_English_Full_lowres.pdf.
  • UNHCR 2021b., Global trends – forced displacement report in 2020. online Available from: https://www.unhcr.org/60b638e37/unhcr-global-trends-2020.
  • UNHCR 2022., UNHCR GIS DATA: Refugee camps and other people of concern’s locations. online, accessed 2022-02-16. Available from: https://www.arcgis.com/home/webmap/viewer.html?webmap=24cad2271eaf4219832bf82da5803193.
  • Unite Nations Department of Economic and Social Affairs (UN DESA), 2020. International migration stock. Available from: https://www.un.org/development/desa/pd/content/international-migrant-stock.
  • Van Den Hoek, J., and Friedrich, H.K., 2021. Satellite-based human settlement datasets inadequately detect refugee settlements: a critical assessment at thirty refugee settlements in Uganda. Remote Sensing, 13 (18), 3574.
  • Venables, W.N., and Ripley, B.D., 2002. Modern applied statistics with s. 4th ed. New York: Springer. ISBN 0-387-95457-0, Available from: https://www.stats.ox.ac.uk/pub/MASS4/.
  • Wang, J., et al., 2022. On the knowledge gain of urban morphology from space. Computers, Environment and Urban Systems, 95, 101831.
  • Weigand, M., et al., 2020. Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution Sentinel-2 data. International Journal of Applied Earth Observation and Geoinformation, 88, 102065.
  • Witmer, F.D.W., 2015. Remote sensing of violent conflict: eyes from above. International Journal of Remote Sensing, 36 (9), 2326–2352.
  • Wurm, M., et al., 2019. Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks. ISPRS Journal of Photogrammetry and Remote Sensing, 150, 59–69.
  • Wurm, M., et al., 2017. Slum mapping in polarimetric SAR data using spatial features. Remote Sensing of Environment, 194, 190–204.
  • Yavcan, B., 2016. On governing the syrian refugee crisis collectively: The view from turkey. online. Available from: http://nearfuturesonline.org/wp-content/uploads/2016/01/Yavcan_04.pdf, accessed 2022-02-10.
  • Zhu, X.X., et al., 2022. The urban morphology on our planet – Global perspectives from space. Remote Sensing of Environment, 269, 112794.

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