This paper addresses the problem of floods classification and floods aftermath detection based onboth 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.

360 panorama video displayed through Virtual reality (VR) glasses or large screens o®ers immersive user experiences, but as such technology becomes commonplace, the need for e±cient streaming methods of such high-bitrate videos arises. In this respect, the attention that 360panorama video has received lately is huge. Many methods have already been proposed, and in this paper, we shed more light on the di®erent trade-o®s in order to save bandwidth while preserving the video quality in the user's ̄eld-of-view (FoV). Using 360 VR content delivered to a Gear VR head-mounted display with a Samsung Galaxy S7 and to a Huawei Q22 set-top- box, we have tested various tiling schemes analyzing the tile layout, the tiling and encoding overheads, mechanisms for faster quality switching beyond the DASH segment boundaries and quality selection con ̄gurations. In this paper, we present an e±cient end-to-end design and real-world implementation of such a 360 streaming system. Furthermore, in addition to researching an on-demand system, we also go beyond the existing on-demand solutions and present a live streaming system which strikes a trade-o® between bandwidth usage and the video quality in the user's FoV. We have created an architecture that combines RTP and DASH, and our system multiplexes a single HEVC hardware decoder to provide faster quality switching than at the traditional GOP boundaries. We demonstrate the performance and illustrate the trade-o®s through real-world experiments where we can report comparable bandwidth savings to existing on-demand approaches, but with faster quality switches when the FoV changes.