  Identification of regions affected by floods is a crucial piece of
information required for better planning and management of post-disaster relief
and rescue efforts. Traditionally, remote sensing images are analysed to
identify the extent of damage caused by flooding. The data acquired from
sensors onboard earth observation satellites are analyzed to detect the flooded
regions, which can be affected by low spatial and temporal resolution. However,
in recent years, the images acquired from Unmanned Aerial Vehicles (UAVs) have
also been utilized to assess post-disaster damage. Indeed, a UAV based platform
can be rapidly deployed with a customized flight plan and minimum dependence on
the ground infrastructure. This work proposes two approaches for identifying
flooded regions in UAV aerial images. The first approach utilizes texture-based
unsupervised segmentation to detect flooded areas, while the second uses an
artificial neural network on the texture features to classify images as flooded
and non-flooded. Unlike the existing works where the models are trained and
tested on images of the same geographical regions, this work studies the
performance of the proposed model in identifying flooded regions across
geographical regions. An F1-score of 0.89 is obtained using the proposed
segmentation-based approach which is higher than existing classifiers. The
robustness of the proposed approach demonstrates that it can be utilized to
identify flooded regions of any region with minimum or no user intervention.
