Optimized Vectorizing of Building Structures With Switch: High-Efficiency Convolutional Channel-Switch Hybridization Strategy

The building planar graph reconstruction, a.k.a. footprint reconstruction, which lies in the domain of computer vision and geoinformatics, has been long afflicted with the challenge of redundant parameters in conventional convolutional models. Therefore, in this letter, we proposed an advanced and adaptive shift architecture, the “Switch” operator, which incorporates nonexponential growth parameters while retaining analogous functionalities to integrate local feature spatial information, resembling a high-dimensional convolution operation. The “Switch” operator, cross-channel operation, architecture implements the XOR operation to exchange adjacent or diagonal features alternately and then blends alternating channels through a
$1 \times 1$
convolution operation to consolidate information from different channels. The SwitchNN architecture, on the other hand, incorporates a group-based parameter-sharing mechanism inspired by the convolutional neural network (CNN) process, thereby significantly reducing the number of parameters. We validated our proposed approach through experiments on the SpaceNet corpus. Our method achieves 82.9% precision, 79.8% F1 score, 83.7% recall, and an MAE of 0.018 with the FLOPs of 24.83 G, outperforming existing state-of-the-art methods, such as Roof-Former and HEAT. These results demonstrate the effectiveness of this innovative architecture in building planar graph reconstruction from 2-D building images.