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Combination of Post-Earthquake LiDAR Data and Satellite Imagery for Buildings Damage Detection
School of Surveying and Geospatial Engineering, College of Eng., University of Tehran, Iran.
School of Surveying and Geospatial Engineering, College of Eng., University of Tehran, Iran.
Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för datavetenskap och samhällsbyggnad, Samhällsbyggnad.ORCID-id: 0000-0003-0192-1533
2019 (engelsk)Inngår i: Earth Observation and Geomatics Engineering, ISSN 2588-4352, Vol. 3, nr 1, s. 12-20Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Earthquakes are known as one of the deadliest natural disasters that have caused many fatalities and homelessness through history. Due to the unpredictability of earthquakes, quick provision of buildings damage maps for reducing the number of losses after an earthquake has become an essential topic in Photogrammetry and Remote Sensing. Low-accuracy building damage maps waste the time that is required to rescue the people in destructed areas by wrongly deploying the rescue teams toward undamaged areas. In this research, an object-based algorithm based on combining LiDAR raster data and high-resolution satellite imagery (HRSI) was developed for buildings damage detection to improve the relief operation. This algorithm combines classification results of both LiDAR raster data and high-resolution satellite imagery (HRSI) for categorizing the area into three classes of “Undamaged,” “Probably Damaged,” and “Surely Damaged” based on the object-level analysis. The proposed method was tested using Worldview II satellite image and LiDAR data of the Port-au-Prince, Haiti, acquired after the 2010 earthquake. The reported overall accuracy of 92% demonstrated the high ability of the proposed method for post-earthquake damaged building detection.

sted, utgiver, år, opplag, sider
University of Tehran , 2019. Vol. 3, nr 1, s. 12-20
Emneord [en]
Earthquake; Building Damage Detection; High Resolution Satellite Image (HRSI); LiDAR
HSV kategori
Identifikatorer
URN: urn:nbn:se:hig:diva-30677DOI: 10.22059/EOGE.2019.278307.1046OAI: oai:DiVA.org:hig-30677DiVA, id: diva2:1353402
Tilgjengelig fra: 2019-09-23 Laget: 2019-09-23 Sist oppdatert: 2019-11-13bibliografisk kontrollert

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