Land-Coverage Aware Path-Planning for Multi-UAV Swarms in Search and Rescue Scenarios

Unmanned Aerial Vehicles (UAVs) have become vital in search-and-rescue (SAR) missions, with autonomous mission planning improving response times and coverage efficiency. Early approaches primarily used path planning techniques such as A*, potential-fields, or Dijkstra's algorithm, while recent approaches have incorporated meta-heuristic frameworks like genetic algorithms and particle swarm optimization to balance competing objectives such as network connectivity, energy efficiency, and strategic placement of charging stations. However, terrain-aware path planning remains under-explored, despite its critical role in optimizing UAV SAR deployments. To address this gap, we present a computer-vision based terrain-aware mission planner that autonomously extracts and analyzes terrain topology to enhance SAR pre-flight planning. Our framework uses a deep segmentation network fine-tuned on our own collection of landcover datasets to transform satellite imagery into a structured, grid-based representation of the operational area. This classification enables terrain-specific UAV-task allocation, improving deployment strategies in complex environments. We address the challenge of irregular terrain partitions, by introducing a two-stage partitioning scheme that first evaluates terrain monotonicity along coordinate axes before applying a cost-based recursive partitioning process, minimizing unnecessary splits and optimizing path efficiency. Empirical validation in a high-fidelity simulation environment demonstrates that our approach improves search and dispatch time over multiple meta-heuristic techniques and against a competing state-of-the-art method. These results highlight its potential for large-scale SAR operations, where rapid response and efficient UAV coordination are critical.