Automatic detection of passable roads after floods in remote sensed and social media data

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

Disaster Analysis using Satellite Image Data with Knowledge Transfer and Semi-Supervised Learning Techniques

With the increase in frequency of disasters and crisis situations like floods, earthquake and hurricanes, the requirement to handle the situation efficiently through disaster response and humanitarian relief has increased. Disasters are mostly unpredictable in nature with respect to their impact on people and property. Moreover, the dynamic and varied nature of disasters makes it difficult to predict their impact accurately for advanced preparation of responses [104]. It is also notable that the economical loss due to natural disasters has increased in recent years, and it, along with the pure humanitarian need, is one of the reasons to research innovative approaches to the mitigation and management of disaster operations efficiently [1]

Multi-source Multimodal Data and Deep Learning for Disaster Response: A Systematic Review.

Mechanisms for sharing information in a disaster situation have drastically changed due to new technological innovations throughout the world. The use of social media applications and collaborative technologies for information sharing have become increasingly popular. With these advancements, the amount of data collected increases daily in different modalities, such as text, audio, video, and images. However, to date, practical Disaster Response (DR) activities are mostly depended on textual information, such as situation reports and email content, and the benefit of other media is often not realised. Deep Learning (DL) algorithms have recently demonstrated promising results in extracting knowledge from multiple modalities of data, but the use of DL approaches for DR tasks has thus far mostly been pursued in an academic context. This paper conducts a systematic review of 83 articles to identify the successes, current and future challenges, and opportunities in using DL for DR tasks. Our analysis is centred around the components of learning, a set of aspects that govern the application of Machine learning (ML) for a given problem domain. A flowchart and guidance for future research are developed as an outcome of the analysis to ensure the benefits of DL for DR activities are utilized.Publishe

Assessing the social impacts of extreme weather events using social media

The frequency and severity of extreme weather events such as flooding, hurricanes/storms and heatwaves are increasing as a result of climate change. There is a need for information to better understand when, where and how these events are impacting people. However, there are currently limited sources of impact information beyond traditional meteorological observations. Social sensing, which is the use of unsolicited social media data to better understand real world events, is one method that may provide such information. Social sensing has successfully been used to detect earthquakes, floods, hurricanes, wildfires, heatwaves and other weather hazards. Here social sensing methods are adapted to explore potential for collecting impact information for meteorologists and decision makers concerned with extreme weather events. After a review of the literature, three experimental studies are presented. Social sensing is shown to be effective for detection of impacts of named storms in the UK and Ireland. Topics of discussion and sentiment are explored in the period before, during and after a storm event. Social sensing is also shown able to detect high-impact rainfall events worldwide, validating results against a manually curated database. Additional events which were not known to this database were found by social sensing. Finally, social sensing was applied to heatwaves in three European cities. Building on previous work on heatwaves in the UK, USA and Australia, the methods were extended to include impact phrases alongside hazard-related phrases, in three different languages (English, Dutch and Greek). Overall, social sensing is found to be a good source of impact information for organisations that need to better understand the impacts of extreme weather. The research described in this project has been commercialised for operational use by meteorological agencies in the UK, including the Met Office, Environment Agency and Natural Resources Wales.Engineering and Physical Sciences Research Council (EPSRC