Abstract
The aim of this research is to provide disaster managers with the results of testing three-dimensional modeling and orthophoto mapping, so as to add value to aerial assessments of flood-related needs and damages. The relevant testing of solutions concerning the real needs of disaster managers is an essential part of the pre-disaster phase. As such, providing evidence-based results of the solutions’ performance is critical with regard to purchasing them and their successful implementation for disaster management purposes. Since disaster response is mostly realized in complex and dynamic, rather than repetitive, environments, it requires pertinent testing methods. A quasi-experimental approach, applied in a form of a full-scale trial meets disaster manager’s requirements as well as addressing limitations resulting from the disaster environment’s characteristics. Three-dimensional modeling and orthophoto mapping have already proven their potential in many professional fields; however, they have not yet been broadly tested for disaster response purposes. Therefore, the objective here is to verify the technologies regarding their applicability in aerial reconnaissance in sudden-onset disasters. The hypothesis assumes that they will improve the efficiency (e.g., time) and effectiveness (e.g., accuracy of revealed data) of this process. The research verifies that the technologies have a potential to facilitate disaster managers with more precise damage assessment; however, their effectivity was less than expected in terms of needs reconnaissance. Secondly, the overall assessment process is heavily burdened by data processing time, however, the technologies allow a reduction of analytical work.
Funder
Seventh Framework Programme
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development
Reference49 articles.
1. Sustainable Development Goalshttps://sustainabledevelopment.un.org/?menu=1300
2. Flood susceptibility mapping using convolutional neural network frameworks
3. DRIVER+ Projecthttps://www.driver-project.eu/
4. ENCIRCLE Projecthttp://encircle-cbrn.eu/
5. RESPONDRONE Projecthttps://respondroneproject.com/
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