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Ronneberger, O., Fischer, P. and Brox, T. (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 234-241.

has been cited by the following article:

• TITLE: Using U-Net to Detect Buildings in Satellite Images

  AUTHORS: Eric Wang, Dali Wang

  KEYWORDS: U-Net, Satellite Images, Computer Vision, Object Detection

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.6, June 30, 2022

  ABSTRACT: This report presented a method that uses deep computing and stochastic gradient descent algorithm to automatically detect building from satellite images. In this method, a convolutional neural network architecture called U-Net was trained to highlight the building pixels from the rest of the image. This method applied a binary cross-entropy loss function, used ADAM algorithm for gradient descent optimization, and adopted interaction-over-union for accuracy measurement. Continuous loss decreases and accuracy increases were observed during the training and validation. Finally, the visualization of the predicted masks from the trained model after 20 epochs proved that the U-Net model delivers over 60% Intersection over Union accuracy results for detecting buildings from satellite images.