Building extraction from remote sensing imagery using SegFormer with post-processing optimization

Traditional methods for building extraction from remote sensing images rely on feature classification techniques, which often suffer from high usage thresholds, cumbersome data processing, slow recognition speeds, and poor adaptability. With the rapid advancement of artificial intelligence, particularly machine learning and deep learning, significant progress has been achieved in the intelligent extraction of remote sensing images. Building extraction plays a crucial role in geographic information applications, such as urban planning, resource management, and ecological protection. This study proposes an efficient and accurate building extraction method based on the SegFormer model, a state-of-the-art Transformer-based architecture for semantic segmentation. The workflow includes data preparation, model construction, model deployment, and application. The SegFormer model is selected for its hierarchical Transformer encoder and lightweight MLP decoder, which enable high-precision binary classification of buildings in remote sensing images. Additionally, post-processing techniques, such as noise filtering, boundary cleanup, and building regularization, are applied to refine the inference results, significantly improving both the visual presentation and accuracy of the extracted buildings. Experimental validation is conducted using the publicly available WHU building dataset, demonstrating the effectiveness of the proposed method in urban, rural, and mountainous areas. The results show that the SegFormer model achieves high accuracy, with the MiT-B5 backbone network reaching 94.13% Intersection over Union (IoU) after 100 training epochs. The study highlights the robustness and scalability of the method, providing a solid technical foundation for remote sensing image analysis and practical applications in geographic information systems.