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The efficient and accurate extraction of building feature information in remote-sensing images has become one of the most important elements of satellite remote-sensing image research. The paper proposes a convolutional neural network with a symmetric encoding-decoding structure. Alternating convolutional blocks and maximum pooled under-sampling at the encoder end are used to complete the relevant operations. The convolutional blocks are operated by linear residual blocks, and complementary zeros are added after 3 × 3 convolutional layers to ensure consistency in feature-map dimensions. A traditional ReLU activation function is replaced with a SELU activation function in order to retain more feature information during training and to solve the problem of dead neurons. A 1 × 1 convolutional layer and a Sigmoid function are finally introduced to complete the final building extraction. The experimental results show that the model is more effective in denselypopulated urban areas than in Alpine towns, but the overcrowding of buildings also causes difficulties in accurate edge segmentation. [ABSTRACT FROM AUTHOR]