This paper addresses the problem of floods classification and floods aftermath detection based on 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 is 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