  Semantic segmentation of aerial videos has been extensively used for decision
making in monitoring environmental changes, urban planning, and disaster
management. The reliability of these decision support systems is dependent on
the accuracy of the video semantic segmentation algorithms. The existing CNN
based video semantic segmentation methods have enhanced the image semantic
segmentation methods by incorporating an additional module such as LSTM or
optical flow for computing temporal dynamics of the video which is a
computational overhead. The proposed research work modifies the CNN
architecture by incorporating temporal information to improve the efficiency of
video semantic segmentation.
  In this work, an enhanced encoder-decoder based CNN architecture (UVid-Net)
is proposed for UAV video semantic segmentation. The encoder of the proposed
architecture embeds temporal information for temporally consistent labelling.
The decoder is enhanced by introducing the feature-refiner module, which aids
in accurate localization of the class labels. The proposed UVid-Net
architecture for UAV video semantic segmentation is quantitatively evaluated on
extended ManipalUAVid dataset. The performance metric mIoU of 0.79 has been
observed which is significantly greater than the other state-of-the-art
algorithms. Further, the proposed work produced promising results even for the
pre-trained model of UVid-Net on urban street scene with fine tuning the final
layer on UAV aerial videos.
