This study focuses on postdisaster damage assessment operations supported by a set of drones. We propose a multistage framework, consisting of two phases applied iteratively to rapidly gather damage information within an assessment period. In the initial phase, the problem involves determining areas to be scanned by each drone and the optimal sequence for visiting these selected areas. We have adapted an electric vehicle routing formulation and devised a variable neighborhood descent heuristic for this phase. In the second phase, information collected from the scanned areas is employed to predict the damage status of the unscanned areas. We have introduced a novel, fast, and easily implementable imputation policy for this purpose. To evaluate the performance of our approach in real-life disasters, we develop a case study for the expected 7.5 magnitude earthquake in Istanbul, Turkey. Our numerical study demonstrates a significant improvement in response time and priority-based metrics.

中文翻译：

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无人机路线预测多级灾后损害评估框架

本研究的重点是由一组无人机支持的灾后损失评估行动。我们提出了一个多阶段框架，由迭代应用的两个阶段组成，以在评估期内快速收集损坏信息。在初始阶段，问题涉及确定每架无人机要扫描的区域以及访问这些选定区域的最佳顺序。我们采用了电动汽车路由公式，并为此阶段设计了可变邻域下降启发式。在第二阶段，利用从扫描区域收集的信息来预测未扫描区域的损坏状态。为此，我们引入了一种新颖、快速且易于实施的插补政策。为了评估我们的方法在现实灾难中的表现，我们针对土耳其伊斯坦布尔预计发生的 7.5 级地震进行了案例研究。我们的数值研究表明响应时间和基于优先级的指标有了显着改善。