  Building extraction is an essential component of study in the science of
remote sensing, and applications for building extraction heavily rely on
semantic segmentation of high-resolution remote sensing imagery. Semantic
information extraction gap constraints in the present deep learning based
approaches, however can result in inadequate segmentation outcomes. To address
this issue and extract buildings with high accuracy, various efficientNet
backbone based U-Net++ has been proposed in this study. The designed network,
based on U-Net, can improve the sensitivity of the model by deep supervision,
voluminous redesigned skip-connections and hence reducing the influence of
irrelevant feature areas in the background. Various effecientNet backbone based
encoders have been employed when training the network to enhance the capacity
of the model to extract more relevant feature. According on the experimental
findings, the suggested model significantly outperforms previous cutting-edge
approaches. Among the 5 efficientNet variation Unet++ based on efficientb4
achieved the best result by scoring mean accuracy of 92.23%, mean iou of
88.32%, and mean precision of 93.2% on publicly available Massachusetts
building dataset and thus showing the promises of the model for automatic
building extraction from high resolution satellite images.
