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

Participatory approach for integrated basin planning with focus on disaster risk reduction : the case of the Limpopo river

This paper defends the idea that a participatory approach is a suitable method for basin planning integrating both water and land aspects. Assertions made are based on scientific literature review and corroborated by field experience and research carried out in the Limpopo River basin, a transboundary river located in southern Africa which is affected by periodical floods. The paper explains how a basin strategic plan can be drafted and disaster risk reduction strategies derived by combining different types of activities using a bottom-up approach, despite an institutional context which operates through traditional top-down mechanisms. In particular, the "Living with Floods" experience in the lower Limpopo River, in Mozambique, is described as a concrete example of a disaster adaptation measure resulting from a participatory planning exercise. In conclusion, the adopted method and obtained results are discussed and recommendations are formulated for potential replication in similar contexts of the developing world

An information assistant system for the prevention of tunnel vision in crisis management

In the crisis management environment, tunnel vision is a set of bias in decision makers’ cognitive process which often leads to incorrect understanding of the real crisis situation, biased perception of information, and improper decisions. The tunnel vision phenomenon is a consequence of both the challenges in the task and the natural limitation in a human being’s cognitive process. An information assistant system is proposed with the purpose of preventing tunnel vision. The system serves as a platform for monitoring the on-going crisis event. All information goes through the system before arrives at the user. The system enhances the data quality, reduces the data quantity and presents the crisis information in a manner that prevents or repairs the user’s cognitive overload. While working with such a system, the users (crisis managers) are expected to be more likely to stay aware of the actual situation, stay open minded to possibilities, and make proper decisions

A role-based software architecture to support mobile service computing in IoT scenarios

The interaction among components of an IoT-based system usually requires using low latency or real time for message delivery, depending on the application needs and the quality of the communication links among the components. Moreover, in some cases, this interaction should consider the use of communication links with poor or uncertain Quality of Service (QoS). Research efforts in communication support for IoT scenarios have overlooked the challenge of providing real-time interaction support in unstable links, making these systems use dedicated networks that are expensive and usually limited in terms of physical coverage and robustness. This paper presents an alternative to address such a communication challenge, through the use of a model that allows soft real-time interaction among components of an IoT-based system. The behavior of the proposed model was validated using state machine theory, opening an opportunity to explore a whole new branch of smart distributed solutions and to extend the state-of-the-art and the-state-of-the-practice in this particular IoT study scenario.Peer ReviewedPostprint (published version

Precision-Aware application execution for Energy-optimization in HPC node system

Power consumption is a critical consideration in high performance computing systems and it is becoming the limiting factor to build and operate Petascale and Exascale systems. When studying the power consumption of existing systems running HPC workloads, we find that power, energy and performance are closely related which leads to the possibility to optimize energy consumption without sacrificing (much or at all) the performance. In this paper, we propose a HPC system running with a GNU/Linux OS and a Real Time Resource Manager (RTRM) that is aware and monitors the healthy of the platform. On the system, an application for disaster management runs. The application can run with different QoS depending on the situation. We defined two main situations. Normal execution, when there is no risk of a disaster, even though we still have to run the system to look ahead in the near future if the situation changes suddenly. In the second scenario, the possibilities for a disaster are very high. Then the allocation of more resources for improving the precision and the human decision has to be taken into account. The paper shows that at design time, it is possible to describe different optimal points that are going to be used at runtime by the RTOS with the application. This environment helps to the system that must run 24/7 in saving energy with the trade-off of losing precision. The paper shows a model execution which can improve the precision of results by 65% in average by increasing the number of iterations from 1e3 to 1e4. This also produces one order of magnitude longer execution time which leads to the need to use a multi-node solution. The optimal trade-off between precision vs. execution time is computed by the RTOS with the time overhead less than 10% against a native execution

Modelling Case-Based Reasoning in Situation-Aware Disaster Management

Situation-aware disaster management (SADIM) is a continuous decision-making and action taking process. This process requires prior knowledge of the ongoing environment and context. SADIM comprises two major processes: situation awareness (SA) - a cognitive process that assesses current situations and anticipate future situations in the environment; and, disaster management (DM) which is a decision-making process preventing, preparing, responding, and recovering for and from a disaster. One of the decision-making technologies used in current SADIM is case-based reasoning (CBR) CBR is used for the disaster management element only. Situation awareness process in current SADIM is carried out using domain rules, statistical reasoning and other methods. This paper therefore presents a method of using CBR to carry out both situation assessment and disaster management decision-making processes in SADIM, building on previous work focusing in SA alone. Using CBR for both processes provides the capability of using past experiences to understand the state of the environment and also solve specific disaster problems. The paper evaluates the method through implementation in disaster prevention in the petroleum drilling domain for early kick detection to prevent a blowout disaster. The results show a clear improvement in similarity assessment and problem solving prediction to prevent blowout