Deep learning techniques are used to achieve state-of-art accuracy in semantic segmentation on aerial ortho-imagery datasets. These algorithms are known to be efficient in terms of accuracy but at the expense of computational power required for training and subsequent inference operations. In this paper we strive to achieve a comparable performance but with lower floating point operations per second (FLOPS) and less training time. With this in mind, we chose to evaluate the EfficientNet-B0 network configured with 5.3 millions parameters and 0.39 billion FLOPS as a feature extractor operating inside a U-net architecture, achieving accuracy levels (mean F1 score of 0.869) comparable to a state-of-the-art deep learning architecture (U-net with Resnet50 as backbone) configured with 25.6 million parameters and 4.1 billion FLOPS which achieved a mean F1 score of 0.87. These promising results demonstrate that employing EfficientNet as the feature extractor in semantic segmentation on aerial ortho-imagery can be an effective strategy, in achieving higher performance results in terms of computational power, especially when running these networks on the edge.