Disaster-Aware Informative Path Planning in Emergency Response Scenarios

Abstract

In emergency response scenarios, rapid acquisition of critical disaster information supports effective decision-making. Traditional geometric coverage-based path planning often struggles to balance efficiency and information value. To address this, we propose a Disaster-Aware Informative Path Planning (DAIPP) method, which integrates a Siamese UNetbased building damage recognition model and formulates a novel information value function that considers recognition results, model uncertainty, and flight cost. We design an improved Frontier-based path planning algorithm, named the Selective Frontier Algorithm (SFA), which enhances the selection of candidate points to achieve the prioritized exploration of critical regions. To validate its effectiveness, the proposed method is compared with coverage path planning, random planning, and Monte Carlo tree search (MCTS). Experiments on the xView2 dataset demonstrate that the proposed method outperforms baselines in terms of information coverage, semantic target hit rate, and weighted information coverage, providing strong support for efficient disaster perception in emergency response.