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.
In this paper, a hierarchical disaster image classification (HDIC) framework based on multi-source data fusion (MSDF) and multiple correspondence analysis (MCA) is proposed to aid emergency managers in disaster response situations. The HDIC framework classifies images into different disaster categories and sub-categories using a pre-defined semantic hierarchy. In order to effectively fuse different sources (visual and text) of information, a weighting scheme is presented to assign different weights to each data resource depending on the hierarchical structure. The experimental analysis demonstrates that the proposed approach can effectively classify disaster images at each logical layer. In addition, the paper also presents an iPad application developed for situation report management using the proposed HDIC framework. The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called “ImageNet”, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond.  Being able to automatically link social media and satellite imagery holds large opportunities for research, with a potentially considerable impact on society. The possibility of integrating different information sources opens in fact to new scenarios where the wide coverage of satellite imaging can be used as a collector of the fine-grained details provided by the social media. Remote-sensed data and social media data can well complement each other, integrating the wide perspective provided by the satellite view with the information collected locally, being it textual, audio, or visual. Among the possible applications, natural disasters are certainly one of the most interesting scenarios, where global and local perspectives are needed at the same time. In this paper, we present a system called JORD that is able to autonomously collect social media data (including the text analysis in local languages) about technological and environmental disasters, and link it automatically to remote-sensed data. Moreover, in order to ensure the quality of retrieved information, JORD is equipped with a hierarchical filtering mechanism relying on the temporal information and the content analysis of retrieved multimedia data. To show the capabilities of the system, we present a large number of disaster events detected by the system, and we evaluate both the quality of the provided information about the events and the usefulness of JORD from potential users viewpoint, using crowdsourcing.