DADCUNet: Dual-Attention Deformable Convolution UNet for Building Extraction

Our study proposes a new approach for building extraction by introducing the Deformable-Convolution Fusion Feature module, based on the Dual-Attention Network and deformable convolution. The core of the algorithm consists of three modules: Auxiliary Encoder, Main Encoder, and Decoder. The Auxiliary Encoder is responsible for linearly embedding and downsampling the input RGB images and utilizes multiple DA Blocks to learn image features, thereby achieving multi-scale feature extraction. Simultane