Building change detection (BCD) from satellite imagery is critical for monitoring urbanization, managing agricultural land, and updating geospatial databases. However, complex variations in building roofs that resemble the background of their surroundings pose challenges for deep-learning-based change detection methods due to their focus on color and texture. Additionally, downsampling can result in the loss of spatial information, leading to incomplete buildings and irregular output boundaries. To address these challenges, a novel Siamese network called AFDE-Net is proposed, which combines differential image features and attention modules using a learnable parameter. The AFDE-Net employs an ensemble spatial-channel attention fusion (ESCAF) module, along with a deep supervision (DS) module, to mitigate the loss of spatial information and refine deep features in high-dimensional inputs. Besides, we have created a new dataset (EGY-BCD) comprising high-resolution and multitemporal satellite images captured in four urban and coastal areas in Egypt to detect building changes. The EGY-BCD dataset includes images with complex types of change, such as tall and dense buildings with roofs that resemble the background of their surroundings, which is a challenge for deep-learning algorithms. The proposed method outperforms other methods on the EGY-BCD dataset with an overall accuracy (OA) of 94.3%, an F1-score of 88.8%, and an mIoU of 86.6%. The datasets and codes will be released at https://github.com/oshholail/EGY-BCD .