{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T10:27:32Z","timestamp":1784543252206,"version":"3.55.0"},"reference-count":53,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2018,8,30]],"date-time":"2018-08-30T00:00:00Z","timestamp":1535587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Exposure is reported to be the biggest determinant of disaster risk, it is continuously growing and by monitoring and understanding its variations over time it is possible to address disaster risk reduction, also at the global level. This work uses Earth observation image archives to derive information on human settlements that are used to quantify exposure to five natural hazards. This paper first summarizes the procedure used within the global human settlement layer (GHSL) project to extract global built-up area from 40 year deep Landsat image archive and the procedure to derive global population density by disaggregating population census data over built-up area. Then it combines the global built-up area and the global population density data with five global hazard maps to produce global layers of built-up area and population exposure to each single hazard for the epochs 1975, 1990, 2000, and 2015 to assess changes in exposure to each hazard over 40 years. Results show that more than 35% of the global population in 2015 was potentially exposed to earthquakes (with a return period of 475 years); one billion people are potentially exposed to floods (with a return period of 100 years). In light of the expansion of settlements over time and the changing nature of meteorological and climatological hazards, a repeated acquisition of human settlement information through remote sensing and other data sources is required to update exposure and risk maps, and to better understand disaster risk and define appropriate disaster risk reduction strategies as well as risk management practices. Regular updates and refined spatial information on human settlements are foreseen in the near future with the Copernicus Sentinel Earth observation constellation that will measure the evolving nature of exposure to hazards. These improvements will contribute to more detailed and data-driven understanding of disaster risk as advocated by the Sendai Framework for Disaster Risk Reduction.<\/jats:p>","DOI":"10.3390\/rs10091378","type":"journal-article","created":{"date-parts":[[2018,8,30]],"date-time":"2018-08-30T10:30:06Z","timestamp":1535625006000},"page":"1378","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Remote Sensing Derived Built-Up Area and Population Density to Quantify Global Exposure to Five Natural Hazards over Time"],"prefix":"10.3390","volume":"10","author":[{"given":"Daniele","family":"Ehrlich","sequence":"first","affiliation":[{"name":"European Commission-Joint Research Centre, Disaster Risk Management Unit, TP. 267, Via E. Fermi 2749, 20127 Ispra, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3009-8868","authenticated-orcid":false,"given":"Michele","family":"Melchiorri","sequence":"additional","affiliation":[{"name":"Piksel s.r.l, Via Breda 176, 20126 Milano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8912-1500","authenticated-orcid":false,"given":"Aneta","family":"Florczyk","sequence":"additional","affiliation":[{"name":"European Commission-Joint Research Centre, Disaster Risk Management Unit, TP. 267, Via E. Fermi 2749, 20127 Ispra, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0620-439X","authenticated-orcid":false,"given":"Martino","family":"Pesaresi","sequence":"additional","affiliation":[{"name":"European Commission-Joint Research Centre, Disaster Risk Management Unit, TP. 267, Via E. Fermi 2749, 20127 Ispra, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3446-8301","authenticated-orcid":false,"given":"Thomas","family":"Kemper","sequence":"additional","affiliation":[{"name":"European Commission-Joint Research Centre, Disaster Risk Management Unit, TP. 267, Via E. Fermi 2749, 20127 Ispra, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christina","family":"Corbane","sequence":"additional","affiliation":[{"name":"European Commission-Joint Research Centre, Disaster Risk Management Unit, TP. 267, Via E. Fermi 2749, 20127 Ispra, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2282-701X","authenticated-orcid":false,"given":"Sergio","family":"Freire","sequence":"additional","affiliation":[{"name":"European Commission-Joint Research Centre, Disaster Risk Management Unit, TP. 267, Via E. Fermi 2749, 20127 Ispra, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3399-3400","authenticated-orcid":false,"given":"Marcello","family":"Schiavina","sequence":"additional","affiliation":[{"name":"European Commission-Joint Research Centre, Disaster Risk Management Unit, TP. 267, Via E. Fermi 2749, 20127 Ispra, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alice","family":"Siragusa","sequence":"additional","affiliation":[{"name":"European Commission-Joint Research Centre, Territorial Development Unit, TP. 263, Via E. Fermi 2749, 20127 Ispra, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,30]]},"reference":[{"key":"ref_1","unstructured":"United Nations Office for Disaster Risk Reduction (UNISDR) (2015). Global Assessment Report 2015, United Nations."},{"key":"ref_2","unstructured":"United Nations Office for Disaster Risk Reduction (UNISDR) (2015). Sendai Framework for Disaster Risk Reduction 2015\u20132030, United Nations International Strategy for Disaster Risk Reduction."},{"key":"ref_3","unstructured":"(2015, December 12). United Nations Treaty Collection \u201cParis Agreement\u201d, Chapter XXVII 7.d. Available online: https:\/\/treaties.un.org\/doc\/Treaties\/2016\/02\/20160215%2006-03%20PM\/Ch_XXVII-7-d.pdf."},{"key":"ref_4","unstructured":"United Nations (2016). Habitat III New Urban Agenda, United Nations."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cardona, O.D., van Aalst, M.K., Birkmann, J., Fordham, M., McGregor, G., Perez, R., Pulwarty, R.S., Schipper, E.L.F., Sinh, B.T., and Decamps, H. (2012). Determinants of risk: Exposure and vulnerability. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, Cambridge University Press.","DOI":"10.1017\/CBO9781139177245.005"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"8074","DOI":"10.1073\/pnas.1231335100","article-title":"A framework for vulnerability analysis in sustainability science","volume":"100","author":"Turner","year":"2003","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s11069-012-0482-0","article-title":"Extracting building stock information from optical satellite imagery for mapping earthquake exposure and its vulnerability","volume":"68","author":"Ehrlich","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dilley, M., Chen, R.S., Deichmann, U., Lam, A.L.L., and Arnold, M. (2005). Natural Disaster Hotspots A Global Risk Analysis, World Bank.","DOI":"10.1596\/0-8213-5930-4"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1080\/014311600210191","article-title":"Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data","volume":"21","author":"Loveland","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3061","DOI":"10.1080\/01431160010007015","article-title":"Census from Heaven: An estimate of the global human population using night-time satellite imagery","volume":"22","author":"Sutton","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.1016\/j.rse.2010.03.003","article-title":"Mapping global urban areas using MODIS 500-m data: New methods and datasets based on \u2018urban ecoregions\u2019","volume":"114","author":"Schneider","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1109\/LGRS.2013.2272953","article-title":"Urban Footprint Processor\u2014Fully Automated Processing Chain Generating Settlement Masks from Global Data of the TanDEM-X Mission","volume":"10","author":"Esch","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1080\/01431161.2012.748992","article-title":"Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data","volume":"34","author":"Gong","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","unstructured":"World Bank (2015). East Asia\u2019s Changing Urban Landscape: Measuring a Decade of Spatial Growth, World Bank."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.gloenvcha.2012.10.002","article-title":"The impact of urbanization on water vulnerability: A coupled human\u2013environment system approach for Chennai, India","volume":"23","author":"Srinivasan","year":"2013","journal-title":"Glob. Environ. Chang."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.rse.2014.09.023","article-title":"Detecting change in urban areas at continental scales with MODIS data","volume":"158","author":"Mertes","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.rse.2011.09.015","article-title":"Monitoring urbanization in mega cities from space","volume":"117","author":"Esch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Melchiorri, M., Florczyk, A., Freire, S., Schiavina, M., Pesaresi, M., and Kemper, T. (2018). Unveiling 25 Years of Planetary Urbanization with Remote Sensing: Perspectives from the Global Human Settlement Layer. Remote Sens., 10.","DOI":"10.3390\/rs10050768"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2015.01.001","article-title":"Global land cover mapping using Earth observation satellite data: Recent progresses and challenges","volume":"103","author":"Ban","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1007\/s11069-014-1538-0","article-title":"Seismic vulnerability assessment of urban environments in moderate-to-low seismic hazard regions using association rule learning and support vector machine methods","volume":"76","author":"Riedel","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3511","DOI":"10.1109\/TGRS.2010.2047260","article-title":"Using Aerial Imagery and GIS in Automated Building Footprint Extraction and Shape Recognition for Earthquake Risk Assessment of Urban Inventories","volume":"48","author":"Sahar","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","unstructured":"Sarabandi, P., and Kiremidjian, A.S. (2007). Development of Algorithms for Building Inventory Compilation through Remote Sensing and Statistical Inferencing, John A. Blume Earthquake Envineering Center, Standford University."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11069-017-2742-5","article-title":"One step back for a leap forward: Toward operational measurements of elements at risk","volume":"86","year":"2017","journal-title":"Nat. Hazards"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"69","DOI":"10.3390\/ijgi1010069","article-title":"Exposure Estimation from Multi-Resolution Optical Satellite Imagery for Seismic Risk Assessment","volume":"1","author":"Wieland","year":"2012","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1785\/0220140130","article-title":"A Multiscale Exposure Model for Seismic Risk Assessment in Central Asia","volume":"86","author":"Wieland","year":"2015","journal-title":"Seismol. Res. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.soildyn.2012.01.003","article-title":"Estimating building inventory for rapid seismic vulnerability assessment: Towards an integrated approach based on multi-source imaging","volume":"36","author":"Wieland","year":"2012","journal-title":"Soil Dyn. Earthq. Eng."},{"key":"ref_27","first-page":"1","article-title":"Towards a cross-border exposure model for the Earthquake Model Central Asia","volume":"58","author":"Wieland","year":"2015","journal-title":"Ann. Geophys."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s11069-016-2663-8","article-title":"Joint use of remote sensing data and volunteered geographic information for exposure estimation: Evidence from Valpara\u00edso, Chile","volume":"86","author":"Riedlinger","year":"2017","journal-title":"Nat. Hazards"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Pesaresi, M., Ehrlich, D., Ferri, S., Florczyk, A., Carneiro, F.S.M., Halkia, S., Julea, A.M., Kemper, T., Soille, P., and Syrris, V. (2016). Operating Procedures for the Production of the Global Human Settlement Layer from Landsat Data of the Epochs 1975, 1990, 2000, and 2014, Joint Research Centre, Publications Office of the European Union.","DOI":"10.1109\/IGARSS.2016.7730897"},{"key":"ref_30","unstructured":"Pesaresi, M., Syrris, V., and Julea, A. (2016, January 15\u201317). Analyzing big remote sensing data via symbolic machine learning. Proceedings of the 2016 Conference on Big Data from Space, Santa Cruz de Tenerife, Spain."},{"key":"ref_31","unstructured":"Sergio, F., MacManus, K., Pesaresi, M., Doxsey-Whitfield, E., and Mills, J. (2016, January 14\u201317). Development of new open and free multi-temporal global population grids at 250 m resolution. Proceedings of the AGILE 2016, Helsinki, Finland."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pesaresi, M., Corbane, C., Julea, A., Florczyk, A., Syrris, V., and Soille, P. (2016). Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas. Remote Sens., 8.","DOI":"10.3390\/rs8040299"},{"key":"ref_33","unstructured":"Pesaresi, M., Ehrlich, D., Kemper, T., Siragusa, A., Florczyk, A., Freire, S., and Corbane, C. (2017). Atlas of the Human Planet 2017: Global Exposure to Natural Hazards, Joint Research Centre, Publications Office of the European Union."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.rse.2011.06.026","article-title":"Forty-year calibrated record of earth-reflected radiance from Landsat: A review","volume":"122","author":"Markham","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/0034-4257(87)90015-0","article-title":"The factor of scale in remote sensing","volume":"21","author":"Woodcock","year":"1987","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.rse.2013.02.026","article-title":"Assessment of the NASA-USGS Global Land Survey (GLS) datasets","volume":"134","author":"Gutman","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A survey of image classification methods and techniques for improving classification performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Pesaresi, M., Syrris, V., and Julea, A. (2016). A new method for Earth Observation Data analyitics based on symbolic Machine learning. Remote Sens., 8.","DOI":"10.3390\/rs8050399"},{"key":"ref_39","unstructured":"Center for International Earth Science Information Network-CIESIN-Columbia University (2017). Gridded Population of the World, Version 4 (GPWv4): Population Count, Revision 10."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/S0065-308X(05)62004-0","article-title":"Determining Global Population Distribution: Methods, Applications and Data","volume":"Volume 62","author":"Balk","year":"2006","journal-title":"Advances in Parasitology"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1080\/23754931.2015.1014272","article-title":"Taking Advantage of the Improved Availability of Census Data: A First Look at the Gridded Population of the World, Version 4","volume":"1","author":"Macmanus","year":"2015","journal-title":"Pap. Appl. Geogr."},{"key":"ref_42","first-page":"1233","article-title":"The GSHAP Global Seismic Hazard Map","volume":"42","author":"Giardini","year":"1999","journal-title":"Ann. Geophys."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1193\/1.1586058","article-title":"Relationships between Peak Ground Acceleration, Peak Ground Velocity, and Modified Mercalli Intensity in California","volume":"15","author":"Wald","year":"1999","journal-title":"Earthq. Spectra"},{"key":"ref_44","unstructured":"Jarvis, A., Reuter, H.I., and Guevara, E. (2018, June 01). Hole-Filled SRTM for the Globe Version 4. Available online: http:\/\/srtm.csi.cgiar.org."},{"key":"ref_45","unstructured":"Hoque, M.M.A., and Khan, S.A.M. (1996, January 24\u201328). Storm surge flooding in Chittagong city and associated risks. Proceedings of the Destructive Water: Water-Caused Natural Disasters, Their Abatement and Control, Anaheim, CA, USA."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.advwatres.2016.05.002","article-title":"Development and evaluation of a framework for global flood hazard mapping","volume":"94","author":"Dottori","year":"2016","journal-title":"Adv. Water Resour."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s11069-004-4546-7","article-title":"National-scale Assessment of Current and Future Flood Risk in England and Wales","volume":"36","author":"Hall","year":"2005","journal-title":"Nat. Hazards"},{"key":"ref_48","unstructured":"Centre for Research on the Epidemeology of Disasters and United Nations International Strategy for Diasaster Reduction (2015). The Human Cost of Weather Related Disasters, Universite\u2019 Catolique de Louvain."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1016\/j.rse.2017.08.035","article-title":"Assessing the accuracy of multi-temporal built-up land layers across rural-urban trajectories in the United States","volume":"204","author":"Leyk","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.rse.2016.03.001","article-title":"Mapping spatial settlement patterns on a global scale: Multi-scale cross-comparison of new and existing global urban maps","volume":"178","author":"Klotz","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_51","unstructured":"Florczyk, A.J., Melchiorri, M., Politis, P., Pesaresi, M., Esch, T., and Ehrlich, D. (2018, January 17\u201319). Analysing Global Consensus on Mapping Human Settlements and Built-Up Area from Space. Proceedings of the 7-th Digital Earth Summit, El Jadida, Morocco."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1007\/s11069-012-0241-2","article-title":"Spatial aspects of building and population exposure data and their implications for global earthquake exposure modeling","volume":"68","author":"Gamba","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_53","unstructured":"UN General Assembly (2015). Transforming Our World: The 2030 Agenda for Sustainable Development, United Nations."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/9\/1378\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:22:06Z","timestamp":1760196126000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/9\/1378"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,30]]},"references-count":53,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2018,9]]}},"alternative-id":["rs10091378"],"URL":"https:\/\/doi.org\/10.3390\/rs10091378","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,30]]}}}