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A web application for the automatic mapping of the flood extent on SAR images

Benoudjit, Abdelhakim and Guida, Raffaella (2018) A web application for the automatic mapping of the flood extent on SAR images In: 4th International Forum on Research and Technologies for Society and Industry (RTSI 2018), 10-13 Sep 2018, Palermo, Italy.

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Abstract

Flood is causing devastating damages every year all over the world. One way to improve the readiness of stakeholders (rescue authorities, policy makers, and communities) is by providing flood extent maps promptly after the disaster, preferably in an automated way and with a minimum number of satellite imagery to reduce costs. The web application developed in this paper aims to address this problem by mapping the flood extent automatically from SAR images.

This web application is portable since it runs on the internet browser, and allows to perform the classification of the flooding in an automated fashion. Another strong point is the rapidity of the processing: the whole processing time was around 3 to 5 minutes for a subset of 20 million pixels. The inundation map returned by our algorithm was validated against vector files mapped by the United Nations Institute for Training and Research (UNITAR) for the same flood event.

Regarding the dataset needed in this study, a pair of a preflood SAR image and an optical image of the same area were used to build a training dataset of water and non-water classes. The learning phase is immediately followed by the classification of the post-flood SAR image into a binary flood map. The web application described in this paper was built with open-source Python libraries which are backed by large communities (Django, Scikit-learn among others). The flood map was eventually displayed on OpenStreetMap maps provided by Mapbox.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Benoudjit, Abdelhakima.benoudjit@surrey.ac.uk
Guida, RaffaellaR.Guida@surrey.ac.uk
Date : 29 November 2018
DOI : 10.1109/RTSI.2018.8548435
Uncontrolled Keywords : Geographic Information System (GIS); Flood extent mapping; Supervised classification; Synthetic Aperture Radar (SAR)
Related URLs :
Depositing User : Clive Harris
Date Deposited : 03 Oct 2018 08:14
Last Modified : 07 Dec 2018 12:41
URI: http://epubs.surrey.ac.uk/id/eprint/849507

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