1 research outputs found
A web application for the automatic mapping of the flood extent on SAR images
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.</p