  In this paper, we present an analysis on the impact of network parameters for
semantic segmentation architectures in context of UAV data processing. We
present the analysis on the DroneDeploy Segmentation benchmark. Based on the
comparative analysis we identify the optimal network architecture to be
FPN-EfficientNetB3 with pretrained encoder backbones based on Imagenet Dataset.
The network achieves IoU score of 0.65 and F1-score of 0.71 over the validation
dataset. We also compare the various architectures in terms of their memory
footprint and inference latency with further exploration of the impact of
TensorRT based optimizations. We achieve memory savings of ~4.1x and latency
improvement of 10% compared to Model: FPN and Backbone: InceptionResnetV2.
