2017
DOI: 10.1088/1755-1315/95/2/022014
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Improving a DSM Obtained by Unmanned Aerial Vehicles for Flood Modelling

Abstract: Abstract. According to the EU flood risks directive, flood hazard map must be used to assess the flood risk. These maps can be developed with hydraulic modelling tools using a Digital Surface Runoff Model (DSRM). During the last decade, important evolutions of the spatial data processing has been developed which will certainly improve the hydraulic models results. Currently, images acquired with Red/Green/Blue (RGB) camera transported by Unmanned Aerial Vehicles (UAV) are seen as a good alternative data source… Show more

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Cited by 24 publications
(23 citation statements)
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“…There are a number of examples of recent data innovations for flood. Examples of early warning measurement technologies include sensors [32] and unmanned aerial vehicles [33]. Examples of advances in historical trend analysis include machine learning [34] and big data analytics [35].…”
Section: Flood Data In the Ukmentioning
confidence: 99%
“…There are a number of examples of recent data innovations for flood. Examples of early warning measurement technologies include sensors [32] and unmanned aerial vehicles [33]. Examples of advances in historical trend analysis include machine learning [34] and big data analytics [35].…”
Section: Flood Data In the Ukmentioning
confidence: 99%
“…Trong những năm gần đây, công nghệ bay chụp không người lái (UAV) đã được ứng dụng khá phổ biến trong nhiều lĩnh vực khác nhau như khảo cổ và bảo tồn di sản văn hóa , quan trắc và bảo vệ môi trường (Alvarado et al, 2015;Feng et al, 2015;Mourato et al, 2017;Oleire-Oltmanns et al, 2012) Panequeet al, 2014;Rokhmana, 2015) và đo đạc địa hình và công trình (Barry & Coakley, 2013;T. D. Bui et al, 2016;Cryderman et al, 2014).…”
Section: Mở đầUunclassified
“…For example, Sullivan et al [75] effectively utilised drones to collect stereo images of streambeds to gather information about the potential threat imposed by large woody debris (LWD) to culverts and bridges. Mourato et al [76] explored the potential of using digital surface models (DSMs) generated from UAV-acquired RGB images as means of achieving optimised digital surface runoff models (DSRMs) which can then be inputted into hydraulic models to reduce spatial data uncertainties that often undermine the accuracy of flood hazard mapping. This entailed the filtering and removal of objects (e.g., buildings, trees and other vegetation) in order to obtain the digital terrain model (DTM) and a normalised digital surface model (nDSM) containing the height values of the objects.…”
Section: Computer Vision and Iot Sensors For Early Warning Systemsmentioning
confidence: 99%