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December 8, 2025PLoS ONEOpen Access

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

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DLDeliang Li

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Overview

Analysis of urban planning and ecological protection using SegFormer achieves high accuracy in remote sensing imagery, suggesting advances in geographic information systems.

Key Points

  • Building extraction demonstrates high accuracy through the SegFormer model, significantly improving remote sensing imagery analysis.
  • The model achieves 94.13% Intersection over Union after 100 training epochs on the WHU building dataset in urban and rural settings.
  • Data processing and semantic segmentation are enhanced by machine learning and deep learning advancements in this approach.
  • The research indicates robust performance, highlighting the potential for effective applications in geographic information systems.

Cite This Study

Deliang Li (2025) studied this question.

synapsesocial.com/papers/694020d72d562116f28fa6cfhttps://doi.org/10.1371/journal.pone.0338104
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Building Extraction on SegFormer Model and Post Processing from Remote Sensing Images2024
  2. 2Building extraction from remote sensing imagery: advanced squeeze-and-excitation residual network based methodology2024
  3. 3A prior knowledge guided deep learning method for building extraction from high-resolution remote sensing images2024 · 10 citations
  4. 4Building Extraction Model and Application from Multi-Scene Remote Sensing Data2026
  5. 5HSDFormer: an improved transformer for remote sensing image semantic segmentation2024 · 1 citations