DEVELOPMENT OF A GEODATABASE FOR EFFICIENT REMOTE SENSING DATA MANAGEMENT IN EMERGENCY SCENARIOS
Disasters such as floods, large fires, landslides, avalanches, or forest fires are often inevitable and cannot be fully prevented, but their impact can be minimized with sound disaster management strategies aided by the latest technological advancements. A key factor affecting these strategies is the time, because any delay can result in dramatic consequences and potentially human losses. Therefore, a quick geo-situation report of the disaster is highly demanded, but still not an easy task because – in most cases – a priori known spatial information like map data or geodatabases, are outdated, and anyway won’t provide an overview on the current situation. This paper provides an exploratory investigation to be smart in providing correct and timely geodata that can help in emergency cases; especially in support decision making in emergency and risk management. In particular, issues related to geodatabase design and visualization of a variety of geodata available play a key role when it comes to efficient data deployment and usability. To this end, a significant part of this research will be devoted to develop a concept for a geodatabase design and dataset management that helps assessing a disaster risk through a potential provision of data needed. Based on this consideration, the proposed concept is to create multi-disciplinary integrated geodatabases as well as an easy-to-use graphical user interface to access the obtained data. To address this concept, hard- and software solutions are being developed through the joint research project ANKommEn and its extension ANKommEn2. In those projects two automated unmanned systems, that is an aerial UAV (Unmanned Aerial Vehicle) and a ground based UGV (Unmanned Ground Vehicle), are being developed to provide up-to-date information of rescue scenarios. Within this paper, highlights about the two project parts will be briefly presented, and then the current state of the art in geospatial database management, followed by focusing on Postgres-based database management connected with QGIS, and finally current results like a Web Map Service will be discussed.