Social sensing is based on the idea that communities or groups of people can provide a set of information similar to those obtainable from a sensor network. Emergency management is a candidate field of application for social sensing. In this work we describe the design, implementation and deployment of a decision support system for the detection and the damage assessment of earthquakes in Italy. Our system exploits the messages shared in real-time on Twitter, one of the most popular social networks in the world. Data mining and natural language processing techniques are employed to select meaningful and comprehensive sets of tweets. We then apply a burst detection algorithm in order to promptly identify outbreaking seismic events. Detected events are automatically broadcasted by our system via a dedicated Twitter account and by email notifications. In addition, we mine the content of the messages associated to an event to discover knowledge on its consequences. Finally we compare our results with official data provided by the National Institute of Geophysics and Volcanology (INGV), the authority responsible for monitoring seismic events in Italy. The INGV network detects shaking levels produced by the earthquake, but can only model the damage scenario by using empirical relationships. This scenario can be greatly improved with direct information site by site. Results show that the system has a great ability to detect events of a magnitude in the region of 3.5, with relatively low occurrences of false positives. Earthquake detection mostly occurs within seconds of the event and far earlier than the notifications shared by INGV or by other official channels. Thus, we are able to alert interested parties promptly. Information discovered by our system can be extremely useful to all the government agencies interested in mitigating the impact of earthquakes, as well as the news agencies looking for fresh information to publish

The advent of online social networks (OSNs) paired with the ubiquitous proliferation of smartphones have enabled social sensing systems. In the last few years, the aptitude of humans to spontaneously collect and timely share context information has been exploited for emergency detection and crisis management. Apart from event-specific features, these systems share technical approaches and architectural solutions to address the issues with capturing, filtering and extracting meaningful information from data posted to OSNs by networks of human sensors. This paper proposes a conceptual and architectural framework for the design of emergency detection systems based on the “human as a sensor” (HaaS) paradigm. An ontology for the HaaS paradigm in the context of emergency detection is defined. Then, a modular architecture, independent of a specific emergency type, is designed. The proposed architecture is demonstrated by an implemented application for detecting earthquakes via Twitter. Validation and experimental results based on messages posted during earthquakes occurred in Italy are reported.

Social Sensing is based on the idea that communities or groups of people provide a set of information similar to those obtainable from a single sensor. This amount of information generate a complex and adequate knowledge of one or more specific issues. A possible field of application for Social Sensing is Emergency Management. By using the Social Media it is possible to gather updated information about emerging situations of danger, in order to gain greater situational awareness and to alert interested parties promptly or verify information obtained through other channels. A system able to timely detect events that are of social concern can be referred to as an Early Warning system. In this work we propose a novel and general architecture for an early warning system and, as a proof-of-concept, we describe an implementation of this architecture for a real scenario. We use Twitter as source of information for the detection of earthquakes on the Italian territory. We compare our results with official data provided by the National Institute of Geophysics and Volcanology, the authority responsible for the monitoring of seismic events in Italy. Results show an high ability of the system in the timely detection of events with magnitude equal or greater than 3.5 Richter with only 10% of False Positives.

The authors of this "Editor's Select" column reflect on an article previously published in IEEE Internet Computing, discussing how social media can and can't be used with regard to disaster-related events.

Over the last years the number of AIS messages generated by ships to signal their position has been increasing thus permitting decision support systems to build new strategies based on the elaboration of such data. In this paper we propose an algorithm based on a K-Nearest Neighbor classifier to predict ships routes. The algorithm was tested on real data extracted from AIS messages collected around Malta. Experiments show that our algorithm reaches a precision of 0.794, a recall of 0.785 and an accuracy of 0.931.

The capability of prompt response in the case of critical circumstances occurring within a maritime scenario depends on the awareness level of the competent authorities. From this perspective, a quick and integrated surveillance service represents a tool of utmost importance. This is even more true when the main purpose is to tackle illegal activities such as smuggling, waste flooding, or malicious vessel trafficking. This work presents an improved version of the OSIRIS system, a previously developed Information and Communication Technology framework devoted to understanding the maritime vessel traffic through the exploitation of optical and radar data captured by satellite imaging sensors. A number of dedicated processing units are cascaded with the objective of (i) detecting the presence of vessel targets in the input imagery, (ii) estimating the vessel types on the basis of their geometric and scatterometric features, (iii) estimating the vessel kinematics, (iv) classifying the navigation behavior of the vessel and predicting its route, and, eventually, (v) integrating the several outcomes within a webGIS interface to easily assess the traffic status inside the considered area. The entire processing pipeline has been tested on satellite imagery captured within the Mediterranean Sea or extracted from public annotated datasets.