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The increased use of UAVs (Unmanned Aerial Vehicles) has heightened demands for an automated landing system intended for a variety of tasks and emergency landings. A key challenge of this system is finding a safe landing site in an unknown environment using on-board sensors. This paper proposes a method to generate a heat map for safety evaluation using images from a single on-board camera. The proposed method consists of the classification of ground surface by CNNs (Convolutional Neural Networks) and the estimation of surface flatness from optical flow. We present the results of applying this method to a video obtained from an on-board camera and discuss ways of improving the method.

Landing Site Detection for UAVs Based on CNNs Classification and Optical Flow from Monocular Camera Images