Multimedia for Disaster Information Management

摘要

It is argued that with the proliferation of social media and smart devices, a considerable amount of multimedia data can be collected before, during, and after a disaster. The disaster-based multimedia data include visual, audio, textual, geographical, sensor, and many other data types extracted from social networks, mobile phones, apps, and emergency websites. This valuable information is considered "big data," which can be further integrated and utilized by the community residents and Emergency Management (EM) responders for better and faster decision-making and for more reliable and accurate situational awareness. Existing multimedia-aided disaster information management systems utilize few sources of information (e.g., textual, Geographic Information System (GIS), sensors, etc.). It is essential to collect all critical information from various sources, summarize, and integrate them to adequately assist the EM. However, multimodal data models for disaster information management are still in its early stages and more efforts are required to achieve this goal. It is possible to generate interactive models for disaster monitoring and situational awareness using visualization techniques. Currently, multimedia techniques and GIS-based models have been proposed to serve this purpose. However, such systems mostly rely on two-dimensional visualizations, which may not be easily interpretable by the non-expert users. Therefore, we can utilize advanced 3D visualization of real-life GIS data to help residents and the community for better preparedness and decision-making. Today, artificial intelligence and machine learning have been extensively used in real-world applications. New technologies and tools such as drones, sensors, and robots can accurately collect data (damaged buildings, trapped victims, etc.) and provide information that cannot be easily gathered by the humans. Also, new machine learning techniques such as deep learning can be leveraged for understanding the deep knowledge of data for planning effective disaster information management and humanitarian relief.