  UAVs have become an essential photogrammetric measurement as they are
affordable, easily accessible and versatile. Aerial images captured from UAVs
have applications in small and large scale texture mapping, 3D modelling,
object detection tasks, DTM and DSM generation etc. Photogrammetric techniques
are routinely used for 3D reconstruction from UAV images where multiple images
of the same scene are acquired. Developments in computer vision and deep
learning techniques have made Single Image Depth Estimation (SIDE) a field of
intense research. Using SIDE techniques on UAV images can overcome the need for
multiple images for 3D reconstruction. This paper aims to estimate depth from a
single UAV aerial image using deep learning. We follow a self-supervised
learning approach, Self-Supervised Monocular Depth Estimation (SMDE), which
does not need ground truth depth or any extra information other than images for
learning to estimate depth. Monocular video frames are used for training the
deep learning model which learns depth and pose information jointly through two
different networks, one each for depth and pose. The predicted depth and pose
are used to reconstruct one image from the viewpoint of another image utilising
the temporal information from videos. We propose a novel architecture with two
2D CNN encoders and a 3D CNN decoder for extracting information from
consecutive temporal frames. A contrastive loss term is introduced for
improving the quality of image generation. Our experiments are carried out on
the public UAVid video dataset. The experimental results demonstrate that our
model outperforms the state-of-the-art methods in estimating the depths.
