An Enhanced Trans-Involution Network for Building Footprint Extraction from High Resolution Orthoimagery

The amalgamation of data visualization and geospatial insights has driven significantly advancements in remote sensing for applications like damage detection and urban planning, particularly building rooftop extraction from high spatial satellite imagery. However, building rooftop extraction using deep learning methods often results in outputs with unclear margin delineation. In this study, we propose a novel approach that combines Transformer architectures, involution, and an enhanced U-net (E-Unet) [1] to improve building footprint extraction performance. Our method demonstrates remarkable accuracy in complex urban environments in the Waterloo Building Dataset. Transformers, renowned for their success in natural language processing, have excelled in adeptness at analyzing sequential data. By using the embedding and multi-head attention blocks, this method is becoming increasingly valuable for building extraction. Involution, in turn, augments neural networks by providing spatial-specific adaptability, effectively extracting inter-band features and surpassing convolutional limitations. Through comprehensive comparative model experiments on the Waterloo building dataset, the optimal architecture was identified. The model significantly enhances accuracy when the Transformer architecture is integrated at the output of the E-Unet. Our proposed network achieves outstanding at the crucial metric values of IoU, mIoU, Precision, F1-score in 81.2, 91.9, 92.9, 89.8 (%), surpassing established frameworks such as FCN-8s, U-Net, DeepLab v3+, Fast Statistical Convolutional Neural Network (SCNN), High-Resolution Net (HRNet) v2, Mask R-CNN, as well as E-Unet.