Robotic surveillance, monitoring, and maintenance problem are open-for-research domains required by the military, industrial facilities, ports, airports, and various indoor and outdoor venues each having different needs. Recent work in path planning of aerial robotics is an emerging field of the surveillance problem, particularly for unstructured or unexplored areas. The nature of path planning problems with different foreign elements like wind, rain, and others escalate the cost of complex computation and power consumption. Due to constraints in payload and endurance, algorithms based on pose-graph, both from the run-time and solution point of view become inefficient when working with unstructured spaces. We propose a simple but effective Clipped Double Q-learning [1] based deep reinforcement learning algorithm (CDDQN)for efficient path planning under the influence of wind and with improved computational efficiency for surveillance in a port area. In the proposed algorithm we have formulated a dense reward structure in consideration of wind's effect on power consumption and time to reach the destination which led to a robust path planning system for high wind scenarios.