{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T09:08:43Z","timestamp":1776071323444,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2020,7,27]],"date-time":"2020-07-27T00:00:00Z","timestamp":1595808000000},"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>For disaster emergency response, timely information is critical and satellite data is a potential source for such information. High-resolution optical satellite images are often the most informative, but these are only available on cloud-free days. For some extreme weather disasters, like cyclones, access to cloud-free images is unlikely for days both before and after the main impact. In this situation, Synthetic Aperture Radar (SAR) data is a unique first source of information, as it works irrespective of weather and sunlight conditions. This paper shows, in the context of the cyclone Idai that hit Mozambique in March 2019, that Change Detection between pairs of SAR data is a perfect match with weather data, and therefore captures impact from the severe cyclone. For emergency operations, the filtering of Change Detections by external data on the location of houses prior to an event allows assessment of the impact on houses as opposed to impact on the surrounding natural environment. The free availability of SAR data from Sentinel-1, with further automated processing of it, means that this analysis is a cost-effective and quick potential first indication of cyclone destruction.<\/jats:p>","DOI":"10.3390\/rs12152409","type":"journal-article","created":{"date-parts":[[2020,7,28]],"date-time":"2020-07-28T10:16:49Z","timestamp":1595931409000},"page":"2409","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Sentinel-1 Change Detection Analysis for Cyclone Damage Assessment in Urban Environments"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1124-3433","authenticated-orcid":false,"given":"David","family":"Malmgren-Hansen","sequence":"first","affiliation":[{"name":"Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kongens Lyngby, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3942-8568","authenticated-orcid":false,"given":"Thomas","family":"Sohnesen","sequence":"additional","affiliation":[{"name":"The World Bank, Washington, DC 20433, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Fisker","sequence":"additional","affiliation":[{"name":"Department of Economics, Development Economics Research Group, University of Copenhagen, DK-1353 Copenhagen, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Javier","family":"Baez","sequence":"additional","affiliation":[{"name":"The World Bank, Washington, DC 20433, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1193\/1.1650865","article-title":"Using high-resolution satellite images for post-earthquake building damage assessment: A study following the 26 January 2001 Gujarat earthquake","volume":"20","author":"Saito","year":"2004","journal-title":"Earthq. 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