Earth observation tools and services to increase the effectiveness of humanitarian assistance

References

• Al Achkar, Z., Baker, I.L., Card, B., & Rayomond, N.A. (2015). Satellite imagery interpretation guide: Intentional burning of tukuls. Cambridge, MA: Harvard Humanitarian Initiative.
   Google Scholar
• Albuquerque, J.P., Herfort, B., & Eckle, M. (2016). The tasks of the crowd: A typology of tasks in geographic information crowdsourcing and a case study in humanitarian mapping. Remote Sensing, 8(10), 859. doi:10.3390/rs8100859
   Web of Science ®Google Scholar
• Almeida, C.M., Oliveira, C.G., Rennó, C.D., & Feitosa, R.Q. (2011). Population estimates in informal settlements using object-based image analysis and 3D modeling. IEEE Earthzine, 16.
   Google Scholar
• Augustin, H., Sudmanns, M., Tiede, D., & Baraldi, A. (2018). A semantic Earth observation data cube for monitoring environmental changes during the Syrian conflict. GI Forum - Journal for Geographic Information Science, 1, 214–227.
   Google Scholar
• Baraldi, A. (2011). Satellite Image Automatic Mapper™ (SIAM™). A turnkey software button for automatic near-real-time multi-sensor multi-resolution spectral rule-based preliminary classification of spaceborne multi-spectral images. Recent Patents on Space Technology, (1), 81–106. doi:10.2174/1877611611101020081
   Google Scholar
• Bjorgo, E. (2000). Refugee camp mapping using very high spatial resolution satellite sensor images. Geocarto International, 15(2), 79–88. doi:10.1080/10106049908542156
   Google Scholar
• Blaschke, T., Hay, G.J., Kelly, M., Lang, S., Hofmann, P., Addink, E., … Tiede, D. (2014). Geographic object-based image analysis: A new paradigm in remote sensing and geographic information science. ISPRS Journal of Photogrammetry and Remote Sensing, 87(1), 180–191. doi:10.1016/j.isprsjprs.2013.09.014
   PubMed Web of Science ®Google Scholar
• Braun, A. (2018). Assessment of building damage in Raqqa during the Syrian civil war using time-series of radar satellite imagery. GI Forum - Journal for Geographic Information Science, 1/2018, 228–242.
   Google Scholar
• Braun, A. (2019). Radar satellite imagery for humanitarian response - bridging the gap between technology and application. Tübingen: University of Tübingen.
   Google Scholar
• Braun, A., Lang, S., & Hochschild, V. (2016). Impact of refugee camps on their environment - a case study using multi-temporal SAR data. Journal of Geography, Environment and Earth Science International, 4(2), 1–17. doi:10.9734/JGEESI
   Google Scholar
• Campbell, J.B. (2002). Introduction to remote sensing. New York: The Guilford Press.
   Google Scholar
• Center for International Earth Science Information Network (CIESIN). (2016). Gridded population of the World, Version 4 (GPWv4). Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC).
   Google Scholar
• Checchi, F., Stewart, B.T., Palmer, J.J., & Grundy, C. (2013). Validity and feasibility of a satellite imagery-based method for rapid estimation of displaced populations. International Journal of Health Geographics, 12(4), 1–12. doi:10.1186/1476-072X-12-4
   PubMedGoogle Scholar
• Chen, J., Zhou, Y., Zipf, A., & Fan, H. (2018). Deep learning from multiple crowds: A case study of humanitarian mapping. IEEE Transactions on Geoscience and Remote Sensing 57(3): 1713-1722.
   Google Scholar
• Denis, G., de Boissezon, H., Hosford, S., Pasco, X., Montfort, B., & Ranera, F. (2016). The evolution of Earth observation satellites in Europe and its impact on the performance of emergency response services. Acta Astronautica, 127, 619–633. doi:10.1016/j.actaastro.2016.06.012
   Web of Science ®Google Scholar
• Di Gregorio, A., & Jansen, L.J.M. (2005). Land cover classification system (LCCS): Classification concepts and user manual. Rome: Food and Agriculture Organisation (FAO).
   Google Scholar
• Ehrlich, D., Lang, S., Laneve, G., Mubareka, S., Schneiderbauer, S., & Tiede, D. (2009). Can Earth observation help to improve information on population? Indirect population estimations from EO derived geospatial data. In B. Jasani, M. Pesaresi, S. Schneiderbauer, & G. Zeug (Eds.), Remote sensing from space supporting international peace and security (pp. 211–237). Berlin: Springer.
   Google Scholar
• Elia, A., Balbo, S., & Boccardo, P. (2018). A quality comparison between professional and crowdsourced data in emergency mapping for potential cooperation of the services. European Journal of Remote Sensing, 51(1), 572–586. doi:10.1080/22797254.2018.1460567
   Web of Science ®Google Scholar
• Elwood, S. (2008). Volunteered geographic information: Key questions, concepts and methods to guide emerging research and practice. GeoJournal, 72, 133–135. doi:10.1007/s10708-008-9187-z
   Google Scholar
• European Commission. (2015). Copernicus emergency management service mapping manual of operational procedures. Brussels: DG GROW, DG ECHO & DG JRC.
   Google Scholar
• European Commission, Joint Research Centre (JRC), & Center for International Earth Science Information Network (CIESIN). (2015). GHS population grid, derived from GPW4, multitemporal (1975, 1990, 2000, 2015).
   Google Scholar
• European Parliament & Council. (2014). Regulation (EU) No 377/2014 establishing the Copernicus Programme and repealing.
   Google Scholar
• Füreder, P., Lang, S., Hagenlocher, M., Tiede, D., Wendt, L., & Rogenhofer, E. (2015). Earth observation and GIS to support humanitarian operations in refugee/IDP camps. In B. Palen, Comes, & Hughes (Eds.), Geospatial Data and Geographical Information Science - Proceedings of the ISCRAM 2015 Conference. Kristiansand.
   Google Scholar
• Füreder, P., Lang, S., Rogenhofer, E., Tiede, D., & Papp, A. (2015). Monitoring displaced people in crisis situations using multi-temporal VHR satellite data during humanitarian operations in South Sudan. In A. Car, T. Jekel, J. Strobl, & G. Griesebner (Eds.), GI_Forum 2015 – Geospatial minds for society (pp. 391–401). Vienna: Verlag der Österreichischen Akademie der Wissenschaften.
   Google Scholar
• German Aerospace Center (DLR). (2016). Global Urban Footprint (GUF) - TSX/TDX - Global. Retrieved from http://g.gdk99ffm.testbed.gdi-de.org/geonetwork/srv/api/records/5fd5bf46-ba85-4672-a4d0-76f322c69201.
   Google Scholar
• Ghorbanzadeh, O., Tiede, D., Dabiri, Z., Sudmanns, M., & Lang, S. (2018). Dwelling extraction in refugee camps using CNN - first experiences and lessons learnt. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII(1), 161–168. doi:10.5194/isprs-archives-XLII-1-161-2018
   Google Scholar
• Giada, S., de Groeve, T., & Ehrlich, D. (2003). Information extraction from very high resolution satellite imagery over Lukole refugee camp, Tanzania. International Journal of Remote Sensing, 24(22), 4251–4266. doi:10.1080/0143116021000035021
   Web of Science ®Google Scholar
• Grundy, C., Füreder, P., Siddique, R., Katsuva Sibongwe, D., Tiede, D., Lang, S., & Checchi, F. (2012) Validation of satellite imagery methods to estimate population size. Paper presented at the MSF Scientific Day, London.
   Google Scholar
• Hagenlocher, M., Lang, S., & Tiede, D. (2012). Integrated assessment of the environmental impact of an IDP camp in Sudan based on very high resolution multi-temporal satellite imagery. Remote Sensing of Environment, 126, 27–38. doi:10.1016/j.rse.2012.08.010
   Web of Science ®Google Scholar
• Harvard Humanitarian Initiative. (2015). Annual report 2015. Technical report. Retrieved from https://hhi.harvard.edu/sites/default/files/hhi_year_end_report_2015_web.pdf
   Google Scholar
• Hofmann, P., Taubenböck, H., & Werthmann, C. (2015). Monitoring and modelling of informal settlements – A review on recent developments and challenges. Paper presented at the Joint Urban Remote Sensing Event (JURSE 2015), Lausanne, Switzerland.
   Google Scholar
• Hu, X., Zhang, Z., & Tao, C. (2004). A robust method for semi-automatic extraction of road centerlines using a piecewise parabolic model and least square template matching. Photogrammetric Engineering & Remote Sensing, 70(12), 1393–1398. doi:10.14358/PERS.70.12.1393
   Web of Science ®Google Scholar
• Jiang, W., He, G., Long, T., & Liu, H. (2017). Ongoing conflict makes Yemen dark: From the perspective of nighttime light. Remote Sensing, 9(8), 798. doi:10.3390/rs9080798
   Web of Science ®Google Scholar
• Kelly, C. (1998). Bridging the gap: Remote sensing and needs assessment—A field experience with displaced populations. Safety Science, 30, 123–129. doi:10.1016/S0925-7535(98)00040-X
   Web of Science ®Google Scholar
• Kemper, T., & Heinzel, J. (2014). Mapping and monitoring of refugees and internally displaced people using EO data. In Q. Weng (Ed.), Global urban monitoring and assessment through earth observation (pp. 195–216). Boca Raton: CRC Press.
   Google Scholar
• Kemper, T., Jenerowicz, M., Gueguen, L., & Poli, D. (2011). Monitoring changes in the Menik Farm IDP camps in Sri Lanka using multi-temporal very high-resolution satellite data. International Journal of Digital Earth, 4, 91–106. doi:10.1080/17538947.2010.512430
   Web of Science ®Google Scholar
• Kranz, O., Sachs, A., & Lang, S. (2015). Assessment of environmental changes induced by internally displaced person (IDP) camps in the Darfur region, Sudan, based on multi-temporal MODIS data. International Journal of Remote Sensing, 36(1), 190–210. doi:10.1080/01431161.2014.999386
   Web of Science ®Google Scholar
• Kulessa, K., & Lang, S. (2016). 3D object-based feature extraction from 3-stereo DSM in urban context. Twente: GEOBIA 2016: Solutions and Synergies.
   Google Scholar
• Laneve, G., Santilli, G., & Lingenfelder, I. (2006). Development of automatic techniques for refugee camps monitoring using very high spatial resolution (VHSR) satellite imagery. IGARSS Conference, Denver, Colorado.
   Google Scholar
• Lang, S., Füreder, P., Kranz, O., Card, B., Roberts, S., & Papp, A. (2015). Humanitarian emergencies: Causes, traits and impacts as observed by remote sensing. In P. Thenkabail (Ed.), Remote sensing handbook (Vol. III, pp. 483–512). Water Resources, Disasters, and Urban. New York: Taylor and Francis.
   Google Scholar
• Lang, S., Füreder, P., & Rogenhofer, E. (2018). Earth observation for humanitarian operations. In C. Al-Ekabi & S. Ferretti (Eds.), Yearbook on Space policy 2016 “Space for sustainable development” (pp. 217–229). Berlin: Springer.
   Google Scholar
• Lang, S., Schoepfer, E., Zeil, P., & Riedler, B. (2017). Earth observation for humanitarian assistance. GI Forum - Journal for Geographic Information Science, 1/2017, 157–165.
   Google Scholar
• Lang, S., Tiede, D., Hölbling, D., Füreder, P., & Zeil, P. (2010). EO-based ex-post assessment of IDP camp evolution and population dynamics in Zam Zam, Darfur. International Journal of Remote Sensing, 31(21), 5709–5731. doi:10.1080/01431161.2010.496803
   Web of Science ®Google Scholar
• Lüthje, F., Tiede, D., & Eisank, C. (2014). Object-based DTM generation from VHR stereo imagery derived DSM data sets. South-Eastern European Journal of Earth Observation and Geomatics, 3, 109–113.
   Google Scholar
• Oak Ridge National Laboratory. 2019. Landscan 2000 Global population database. Retrieved from http://sedac.ciesin.columbia.edu/plue/gpw/landscan.
   Google Scholar
• Puissant, A., Zhang, W., & Skupinski, G. (2012). Urban morphology analysis by high and very high spatial resolution remote sensing. - Rio de Janeiro - Brazil. Proceedings of the 4th GEOBIA, Rio de Janeiro.
   Google Scholar
• Quinn, J.A., Nyhan, M.M., Navarro, C., Coluccia, D., Bromley, L., & Luengo-Oroz, M. (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, 376(2128). doi:10.1098/rsta.2017.0363
   Google Scholar
• Riedler, B., Bäuerl, M., Wendt, L., Kulessa, K., & Öze, A. (2017). Remote sensing applications to support rehabilitation of water infrastructure in Lapilang, Nepal. GI Forum - Journal for Geographic Information Science, 2017(1), 183–198. doi:10.1553/giscience2017_01_s183
   Google Scholar
• SBTF. (2011). Crowdsourcing satellite imagery analysis for UNHCR-Somalia: Latest results. http://blog.standbytaskforce.com/2011/11/10/unhcr-somalia-latest-results/
   Google Scholar
• School of Geography and Environmental Science - Univ Southampton, Dept of Geography and Geosciences - Univ Louisville, Dept de Geographie - Univ Namur, & Center for International Earth Science Information Network (CIESIN). (2018). WorldPop - Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).
   Google Scholar
• Schörghofer, R., Lang, S., Wendt, L., & Riedler, B. (2017). CMaP - collaborative mapping platform for humanitarian organisations. GI Forum - Journal for Geographic Information Science, 1/2017, 207–216.
   Google Scholar
• Spröhnle, K., Fuchs, E.M., & Pelizari, P.A. (2017). Object-based analysis and fusion of optical and SAR satellite data for dwelling detection in refugee camps. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 99, 1–12.
   Google Scholar
• Spröhnle, K., Tiede, D., Schoepfer, E., Füreder, P., Svanberg, A., & Rost, T. (2014). Earth observation-based dwelling detection approaches in a highly complex refugee camp environment — A comparative study. Remote Sensing, 6(10), 9277–9297. doi:10.3390/rs6109277
   Web of Science ®Google Scholar
• Stängel, M., Tiede, D., Lüthje, F., Füreder, P., & Lang, S. (2014). Object-based image analysis using VHR satellite imagery for monitoring the dismantling of a refugee camp after a crisis: The case of Lukole, Tanzania. In R. Vogler, A. Car, J. Strobl, & G. Griesebner (Eds.), GI_Forum 2014. Geospatial innovation for society (pp. 45–48). Wien/Berlin: Verlag der Österreichischen Akademie der Wissenschaften/VDE Verlag GmbH.
   Google Scholar
• Stevens, F.R., Gaughan, A.E., Linard, C., & Tatem, A.J. (2015). Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PloS One, 10(2), e0107042. doi:10.1371/journal.pone.0107042
   PubMed Web of Science ®Google Scholar
• Sudmanns, M., Tiede, D., Wendt, L., & Baraldi, A. (2017). Automatic ex-post flood assessment using long time series of optical Earth observation images. GI Forum - Journal for Geographic Information Science, 2017/1, 217–227. doi:10.1553/giscience2017_01_s217
   Google Scholar
• Tiede, D., Baraldi, A., Sudmanns, M., Belgiu, M., & Lang, S. (2017). Architecture and prototypical implementation of a semantic querying system for big Earth observation image bases. European Journal of Remote Sensing, 50, 452–463. doi:10.1080/22797254.2017.1357432
   PubMed Web of Science ®Google Scholar
• Tiede, D., Füreder, P., Lang, S., Hölbling, D., & Zeil, P. (2013). Automated analysis of satellite imagery to provide information products for humanitarian relief operations in refugee camps – From scientific development towards operational services. PFG Photogrammetrie - Fernerkundung - Geoinformation, 3, 185–195. doi:10.1127/1432-8364/2013/0169
   Google Scholar
• Tiede, D., Krafft, P., Füreder, P., & Lang, S. (2017). Stratified template matching to support refugee camp analysis in OBIA workflows. Remote Sensing, 9(326), 326. doi:10.3390/rs9040326
   Google Scholar
• UNHCR. (2017). Global Trends: Forced Displacement iin 2017. http://www.unhcr.org/5b27be547.pdf
   Google Scholar
• United Nations. (2014). World Urbanization Prospects: The 2014 revision: Highlights; ST/ESA/SER.A/352. United Nations: New York.
   Google Scholar
• Voigt, S., Giulio-Tonolo, F., Lyons, J., Kučera, J., Jones, B., Schneiderhan, T., … Guha-Sapir, D. (2016). Global trends in satellite-based emergency mapping. Science, 253(6296), 247–252. doi:10.1126/science.aad8728
   Google Scholar
• Voigt, S., Kemper, T., Riedlinger, T., Kiefl, R., Scholte, K., & Mehl, H. (2007). Satellite image analysis for disaster and crisis-management support. IEEE Transactions on Geoscience and Remote Sensing, 45(6), 1520–1528. doi:10.1109/TGRS.2007.895830
   Web of Science ®Google Scholar
• Voigt, S., Schoepfer, E., Fourie, C., & Mager, A. (2014). Towards semi-automated satellite mapping for humanitarian situational awareness. San Jose, CA: Global Humanitarian Technology Conference (GHTC).
   Google Scholar
• Wendt, L., Hilberg, S., Robl, J., Hochschild, V., Rogenhofer, E., Füreder, P., … Zeil, P. (2014). Assisting the exploration of groundwater near refugee/IDP camps using remote sensing and GIS. Gemeinsame Tagung 2014 der DGfK, der DGPF, der GfGI und des GiN, Hamburg.
   Google Scholar
• Wendt, L., Lang, S., & Rogenhofer, E. (2017). Monitoring of refugee and IDP camps with Sentinel 2 imagery – A feasibility study. GI Forum - Journal for Geographic Information Science, 1/2017, 172–182.
   Google Scholar
• Witmer, F.D.W., & O’Loughlin, J. (2011). Detecting the effects of wars in the caucasus regions of Russia and Georgia using radiometrically normalized DMSP-OLS nighttime lights imagery. Remote Sensing, 48, 478–500. doi:10.2747/1548-1603.48.4.478
   Web of Science ®Google Scholar
• Zeil, P., Ourevitch, S., Debien, A., & Pico, U. (2017). The copernicus user uptake – Copernicus relays and copernicus academy. GI Forum - Journal for Geographic Information Science, (1), 253–259.
   Google Scholar
• Zhu, X.X., Tuia, D., Mou, L., Xia, G.S., Zhang, L., Xu, F., & Fraundorfer, F. (2017). Deep learning in remote sensing - a review. IEEE Geoscience and Remote Sensing Magazine, 5(4), 8–36. doi:10.1109/MGRS.2017.2762307
   Web of Science ®Google Scholar