  Urban buildings are extracted from high-resolution Earth observation (EO)
images using semantic segmentation networks like U-Net and its successors. Each
re-iteration aims to improve performance by employing a denser skip connection
mechanism that harnesses multi-scale features for accurate object mapping.
However, denser connections increase network parameters and do not necessarily
contribute to precise segmentation. In this paper, we develop three dual skip
connection mechanisms for three networks (U-Net, ResUnet, and U-Net3+) to
selectively deepen the essential feature maps for improved performance. The
three mechanisms are evaluated on feature maps of different scales, producing
nine new network configurations. They are evaluated against their original
vanilla configurations on four building footprint datasets of different spatial
resolutions, including a multi-resolution (0.3+0.6+1.2m) dataset that we
develop for complex urban environments. The evaluation revealed that densifying
the large- and small-scale features in U-Net and U-Net3+ produce up to 0.905
F1, more than TransUnet (0.903) and Swin-Unet (0.882) in our new dataset with
up to 19x fewer parameters. The results conclude that selectively densifying
feature maps and skip connections enhances network performance without a
substantial increase in parameters. The findings and the new dataset will
contribute to the computer vision domain and urban planning decision processes.
