Automatic identification of the landing area is crucial for UAV (Unmanned Aerial Vehicles) to land correctly and safely. Using passive vision sensors to achieve this objective is a very promising avenue due to their low cost and the potential they provide for performing simultaneous terrain analysis. In this paper, a computer vision method is proposed using an improved U-Net based architecture on UAV imagery to assess the safe landing area. Contrary to past methods, which little attention has been paid to the whole landing process with multiple descending altitude, experiment involves evaluating the landing area by analyzing visual images obtained from different descending heights. In the initial stage of landing, separation was made between water and land, guiding the flight to the land. As descending, classification was applied on images captured near ground into safe/unsafe landing areas and then mapped to the safety score for selection. Experiments on public datasets have shown promising results.