The extraction of building footprints based on high-resolution remote sensing images is widely used in many fields such as land survey and urban planning, and high resolution also brings complex background information. In order to improve the effectiveness of building footprint extraction, this paper proposes a RefineSegFormer that combines a hierarchical transformer encoder and a cross-level feedback Refine feature pyramid network implemented through deformable convolution. RefineSegFormer achieved an F1 score of 96.55% and an IoU of 93.33% on the widely recognized WHU building data set, an F1 of 93.19% and an IoU of 87.25% on the 0.3-m Mandalay City data set, and an F1 of 90.53% and an IoU of 82.70% on 2-m Dingri County data set, all of which achieved the best results, proving the effectiveness and practical application value of the proposed model in this paper. The improved lightweight model for building footprint extraction has better performance than the unimproved medium model. In submeter remote sensing image building footprint extraction, the parameters are only 20% of the medium model, which can achieve more than 98% of the performance of the improved medium model, balancing extraction accuracy and inference speed. In the extraction of building footprints from meter remote sensing images with a small number of buildings and high similarity between buildings and the background, it is more recommended to use the improved medium model. Compared with the unimproved medium model, F1 has increased by 6.71%, and IoU has increased by 10.55%. The model and code of RefineSegFormer can be available at: https://github.com/amanforinteresting/RefineSegFormer.
Yang et al. (Thu,) studied this question.