This paper address the problem area of Unmanned Aerial Vehicles (UAV) emergency scenarios in which forced or emergency landing becomes imperative. Emergency or forced landing becomes crucial when there is system failure which impacts the flight safety and UAV is unable to fly back to the emergency landing runway. This failure could be due to data link loses, GPS failure, engine or flight surface failure. Forced landing needs to be performed on safe landing site which could be plane surface, open fields or grounds. First step to accomplish the successful forced landing safely is to search and select the safe landing site. This article presents the system design which assists the UAV in selection of safe landing site having no obstacles, buildings and trees. The proposed system design uses computer vision and machine learning techniques in order to classify feasible and non-feasible landing sites. The proposed algorithms in this article also incorporate the scenarios having low lighting conditions due to clouds. The system has been designed and simulated in MATLAB and promising results have been achieved with very less processing time and computational power.

Unmanned Aerial Vehicles (UAVs) rely on navigation commands from autonomous flight control system or from Ground Control System (GCS) via line-ofsight wireless data link. UAV needs to perform immediate landing on predefined airfield in case of extreme emergency like navigation, data link, engine or control surface failure, that cannot be accomplished in some cases and accidents can occur which can result collateral damage as well. This paper presents the system design which can discover the appropriate area within the surroundings for immediate landing in case of emergency. The proposed system design consists of two stages. During first stage, system takes top view images from UAV onboard camera, then image processing algorithm extracts and refine the attributes of the image. In second stage, machine learning based algorithm evaluates the results from previous stage, and based on its previous training, decides whether the area visible in image is good for safe landing or not. We implement proposed system design in MATLAB and the approach used is validated with experimental results on test data. Proposed system design uses combination of simple techniques, which makes it less computationally intensive, having reduced latency, low implementation cost and easy to implement on high speed real time hardware like FPGA/ASIC. 