Deep learning (DL) techniques for the precise semantic segmentation of aerial imagery have remained a challenge because of the vague boundaries of target objects caused by the low resolution of images. Despite the improved segmentation performance using up/downsampling operations in early DL models, conventional operators cannot fully preserve spatial information and thus generate vague boundaries of target objects. Therefore, for the precise segmentation of target objects in aerial images, this paper presents two novel operators: (1) upsampling interpolation method (USIM), an operator that upsamples input feature maps and combines feature maps into one while preserving the spatial information of both inputs, and (2) USIM gate (UG), an advanced USIM operator with boundary-attention mechanisms. The experimental results demonstrate that using the USIM and UG with state-of-the-art DL models can improve the segmentation performance with clear boundaries of target objects (Intersection over Union: +6.9%; BJ: +10.1%). Furthermore, mathematical proofs verify that the USIM and UG contribute to the handling of spatial information.