Application of Image Analytics for Disaster Response in Smart Cities

Post-disaster, city planners need to effectively plan response activities and assign rescue teams to specific disaster zones quickly. We address the problem of lack of accurate information of the disaster zones and existence of human survivors in debris using image analytics from smart city data. Innovative usage of smart city infrastructure is proposed as a potential solution to this issue. We collected images from earthquake-hit smart urban environments and implemented a CNN model for classification of these images to identify human body parts out of the debris. TensorFlow backend (using Keras) was utilized for this classification. We were able to achieve 83.2% accuracy from our model. The novel application of image data from smart city infrastructure and the resultant findings from our model has significant implications for effective disaster response operations, especially in smart cities

Introduction to the Minitrack on Disaster Information, Technology, and Resilience in Digital Government

International audienceThe 21st Century has been termed "the century of disasters." Worldwide there were twice as many disasters and catastrophes in the first decade of this century as in the last decade of the 20th Century. All continents are affected, both directly and indirectly. And the trend continues, fuelled by climate change, demographic changes and social dynamics. The serious challenges facing government in cities, regions and nations of the world relate to acute shocks (such as forest fires, floods, earthquakes, tsunamis, pandemics and terrorist attacks) and chronic stresses (such as high unemployment, religious extremism, inefficient public transport systems, endemic violence, chronic shortages of food and water). Information is among the key life-supporting essentials in a disaster response, as well as water and basic foods which are vital to sustain lives. It is information technology these days that gives us access to most of this information. We rely greatly on it. In this sense, information management with effective use of information systems should be conducted and evaluated among disaster relief agencies. Successful information management will result in making higher situational awareness in a field that is crucial for a disaster response. It also guides us to build a disaster-resilient community which can adapt the society to those unexpected events. These issues should be tackled at each level of the governance (international, national, regional, local, etc.), and with regards to all relevant dimensions (social, technological, interoperability, agility, etc.). This minitrack features government and disaster information management, including the development of disaster resilience communities/societies. Five papers have been selected that deal with any aspect of the analysis, design, development, deployment, implementation, integration, operation, use or evaluation of ICT for discussing government roles for disaster responses, disaster information management, and resilience communities. In addition, we support innovative and breakthrough visions regarding "disaster information, technology and resilience.

A deep multi-modal neural network for informative Twitter content classification during emergencies

YesPeople start posting tweets containing texts, images, and videos as soon as a disaster hits an area. The analysis of these disaster-related tweet texts, images, and videos can help humanitarian response organizations in better decision-making and prioritizing their tasks. Finding the informative contents which can help in decision making out of the massive volume of Twitter content is a difficult task and require a system to filter out the informative contents. In this paper, we present a multi-modal approach to identify disaster-related informative content from the Twitter streams using text and images together. Our approach is based on long-short-term-memory (LSTM) and VGG-16 networks that show significant improvement in the performance, as evident from the validation result on seven different disaster-related datasets. The range of F1-score varied from 0.74 to 0.93 when tweet texts and images used together, whereas, in the case of only tweet text, it varies from 0.61 to 0.92. From this result, it is evident that the proposed multi-modal system is performing significantly well in identifying disaster-related informative social media contents