Deep Convolutional Neural Network for Flood Extent Mapping Using Unmanned Aerial Vehicles Data

Flooding is one of the leading threats of natural disasters to human life and property, especially in densely populated urban areas. Rapid and precise extraction of the flooded areas is key to supporting emergency-response planning and providing damage assessment in both spatial and temporal measure...

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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 19; H. 7; S. 1486
Hauptverfasser:	Gebrehiwot, Asmamaw, Hashemi-Beni, Leila, Thompson, Gary, Kordjamshidi, Parisa, Langan, Thomas E.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI 27.03.2019 MDPI AG
Schlagworte:	convolutional neural networks floodplain mapping fully convolutional network geospatial data processing remote sensing unmanned aerial vehicles floodplain mapping fully convolutional network remote sensing convolutional neural networks unmanned aerial vehicles geospatial data processing
ISSN:	1424-8220, 1424-8220
Online-Zugang:	Volltext
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