Some of these projects are further described as follows.
Natural disasters, as floods and storms, are phenomena that cause great harm to people in urban and rural areas, motivating prevention and reaction actions to these accidents. Such actions must consider several information sources in different scenarios to generate good results. In this context, the aim of this work is to develop a flexible geospatial architecture to provide information to authorities (such as the Civil Defense) and communities with the goal of supporting decision-making that aims to increase the resilience of the population against floods. See research projects.
Volunteered Geographic Information (VGI) has emerged as a potential source of geographic information for different domains. Despite the many advantages associated with it, such information lacks quality assurance, since it is provided by individuals with different motivations and backgrounds. In response to this, several methods have been proposed to assess the quality of volunteered geographic information of different platforms. However, there has been little investigation aimed at explaining how cross-platform data could be used for quality assessment in the context of flood management. Moreover, it is not clear how the volunteer could be inserted in the quality assessment process in order to improve the overall quality. In this work, we propose an approach to assess the quality of volunteered geographic information in flood management domain that combines cross-platform data, i.e. OpenStreetMap and social media data, and authoritative data. See the research projects.
The research projects associated with this component aim at designing and developing approaches that can be employed by emergency organizations for supporting (or improving) their decision-making. These approaches might be innovative information visualization techniques, new models for representing real-world elements and their relationships, or modern architectures of information systems. For this, they should take into consideration the integrated and high-quality data provided by heterogeneous data sources (e.g. sensors and volunteered information) and particular requirements of decision-makers. See the research projects
The traditional approach of hydrological forecasting in real time considers that the input, state and output processes of the hydrological model are subject to dynamic uncertainties that are updated continuously. Decisions have to be taken in real time considering not only uncertainty but also the perception of vulnerable communities. Precisely, the community has to play a major role in the management of natural disasters as they are directly affected. Therefore, the work focuses on flood forecasting, using both official data and data provided for by the population in order to reduce the uncertainty in forecasting floods. Additionally, it is intended that the method can be implemented in real-time to different catchments in Brazil. See the research projects
In the aftermath of a disaster, there is an urgent need for maps to support humanitarian efforts, especially in developing countries. In response to this, the OpenStreetMap (OSM) project
has been used in order to produce disaster-affected areas maps in a collaborative way, mainly in the response phase. Despite the increasing use in Disaster Management, it is still incipient research projects in order to understand how the dynamic collaboration between the OSM community, decision makers and local community affected by a disaster occurs. In this context, this work proposes a process-model-based approach to structure the collaborative mapping activities and to improve its situational awareness in crisis management domain. See the research projects