Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Cross-comparative analysis of evacuation behavior after earthquakes using mobile phone data

Despite the importance of predicting evacuation mobility dynamics after large scale disasters for effective first response and disaster relief, our general understanding of evacuation behavior remains limited because of the lack of empirical evidence on the evacuation movement of individuals across multiple disaster instances. Here we investigate the GPS trajectories of a total of more than 1 million anonymized mobile phone users whose positions are tracked for a period of 2 months before and after four of the major earthquakes that occurred in Japan. Through a cross comparative analysis between the four disaster instances, we find that in contrast with the assumed complexity of evacuation decision making mechanisms in crisis situations, the individuals' evacuation probability is strongly dependent on the seismic intensity that they experience. In fact, we show that the evacuation probabilities in all earthquakes collapse into a similar pattern, with a critical threshold at around seismic intensity 5.5. This indicates that despite the diversity in the earthquakes profiles and urban characteristics, evacuation behavior is similarly dependent on seismic intensity. Moreover, we found that probability density functions of the distances that individuals evacuate are not dependent on seismic intensities that individuals experience. These insights from empirical analysis on evacuation from multiple earthquake instances using large scale mobility data contributes to a deeper understanding of how people react to earthquakes, and can potentially assist decision makers to simulate and predict the number of evacuees in urban areas with little computational time and cost, by using population density information and seismic intensity which can be observed instantaneously after the shock

Knowledge acquisition and dissemination for emergency situation

Emergency situation is highly uncertain, dynamic, time pressure in making decisions and involves multi organizations and multi jurisdiction level. This paper presents a conceptual architecture that can be used by emergency response task force in assisting the victims of the disaster. Flood disaster is used as a case study. The architecture describes the knowledge and communication for flood emergency response management

Handbook of Emergency Management For State-Level Transportation Agencies, MTI Report 09-10

The Department of Homeland Security has mandated specific systems and techniques for the management of emergencies in the United States, including the Incident Command System, the National Incident Management System, Emergency Operations Plans, Emergency Operations Centers, Continuity of Government Plans and Continuity of Operations Plans. These plans and systems may be applied to the state-level transportation agency�s disaster response systems to enhance efficiency and effectiveness. Specific guidance and management techniques are provided to aid emergency planning staff to create DHS-compliant systems

Research Directions in Information Systems for Humanitarian Logistics

This article systematically reviews the literature on using IT (Information Technology) in humanitarian logistics focusing on disaster relief operations. We first discuss problems in humanitarian relief logistics. We then identify the stage and disaster type for each article as well as the article’s research methodology and research contribution. Finally, we identify potential future research directions

Distributed drone base station positioning for emergency cellular networks using reinforcement learning

Due to the unpredictability of natural disasters, whenever a catastrophe happens, it is vital that not only emergency rescue teams are prepared, but also that there is a functional communication network infrastructure. Hence, in order to prevent additional losses of human lives, it is crucial that network operators are able to deploy an emergency infrastructure as fast as possible. In this sense, the deployment of an intelligent, mobile, and adaptable network, through the usage of drones—unmanned aerial vehicles—is being considered as one possible alternative for emergency situations. In this paper, an intelligent solution based on reinforcement learning is proposed in order to find the best position of multiple drone small cells (DSCs) in an emergency scenario. The proposed solution’s main goal is to maximize the amount of users covered by the system, while drones are limited by both backhaul and radio access network constraints. Results show that the proposed Q-learning solution largely outperforms all other approaches with respect to all metrics considered. Hence, intelligent DSCs are considered a good alternative in order to enable the rapid and efficient deployment of an emergency communication network

Towards connecting people, locations and real-world events in a cellular network

The success of personal mobile communication technologies has led an emerging expansion of the telecommunication infrastructure but also to an explosion to mobile broadband data traffic as more and more people completely rely on their mobile devices, either for work or entertainment. The continuously interaction of their mobile devices with the mobile network infrastructure creates digital traces that can be easily logged by the network operators. These digital traces can be further used, apart from billing and resource management, for large-scale population monitoring using mobile traffic analysis. They could be integrated into intelligent systems that could help at detecting exceptional events such as riots, protests or even at disaster preventions with minimal costs and improve people safety and security, or even save lives. In this paper we study the use of fully anonymized and highly aggregate cellular network data, like Call Detail Records (CDRs) to analyze the telecommunication traffic and connect people, locations and events. The results show that by analyzing the CDR data exceptional spatio-temporal patterns of mobile data can be correlated to real-world events. For example, high user network activity was mapped to religious festivals, such as Ramadan, Le Grand Magal de Touba and the Tivaouane Maouloud festival. During the Ramadan period it was noticed that the communication pattern doubled during the night with a slow start during the morning and along the day. Furthermore, a peak increase in the number of voice calls and voice calls duration in the area of Kafoutine was mapped to the Casamance Conflict in the area which resulted in four deaths. Thus, these observations could be further used to develop an intelligent system that detects exceptional events in real-time from CDRs data monitoring. Such system could be used in intelligent transportation management, urban planning, emergency situations, network resource allocation and performance optimization, etc