  This paper addresses the problem of floods classification and floods
aftermath detection utilizing both social media and satellite imagery.
Automatic detection of disasters such as floods is still a very challenging
task. The focus lies on identifying passable routes or roads during floods. Two
novel solutions are presented, which were developed for two corresponding tasks
at the MediaEval 2018 benchmarking challenge. The tasks are (i) identification
of images providing evidence for road passability and (ii) differentiation and
detection of passable and non-passable roads in images from two complementary
sources of information. For the first challenge, we mainly rely on object and
scene-level features extracted through multiple deep models pre-trained on the
ImageNet and Places datasets. The object and scene-level features are then
combined using early, late and double fusion techniques. To identify whether or
not it is possible for a vehicle to pass a road in satellite images, we rely on
Convolutional Neural Networks and a transfer learning-based classification
approach. The evaluation of the proposed methods are carried out on the
large-scale datasets provided for the benchmark competition. The results
demonstrate significant improvement in the performance over the recent
state-of-art approaches.
