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

ABSTRACT: 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 prep

SUBMITTER: Li D 

PROVIDER: S-EPMC12685217 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature