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HR-Mamba: Building Footprint Segmentation with Geometry-Driven Boundary Regularization.

Buyu Su1, Defei Yin2, Piyuan Yi1

  • 1National Key Laboratory of Uranium Resource Exploration-Mining and Nuclear Remote Sensing, Beijing Research Institute of Uranium Geology, Beijing 100029, China.

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|January 28, 2026
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Summary
This summary is machine-generated.

High-Resolution-Mamba (HR-Mamba) improves building extraction in dense urban areas by combining High-Resolution Network (HRNet) with advanced modeling. This method enhances accuracy and produces smoother, engineering-ready building outlines.

Keywords:
HR-MambaMamba-SSMbuilding semantic segmentationgeometry-driven post-processinghigh-resolution remote sensing

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Area of Science:

  • Remote Sensing
  • Computer Vision
  • Geospatial Analysis

Background:

  • Building extraction is crucial for urban planning and land-use assessment.
  • Dense urban scenes present challenges like missed objects, adhesion, and irregular contours in current methods.

Purpose of the Study:

  • To develop a high-resolution semantic segmentation network for improved building extraction.
  • To address limitations in dense urban environments, focusing on detail fidelity and contour regularity.

Main Methods:

  • Introduced High-Resolution-Mamba (HR-Mamba), augmenting HRNet with edge-aware and sequence-state modeling.
  • Employed a Canny-enhanced stem, involution-based blocks, and Mamba-based State Space Models (Mamba-SSM) for boundary stabilization and long-range dependency capture.
  • Utilized a composite loss function (BCE, Dice, Boundary loss) and a feature-driven adaptive post-processing pipeline for refined building outlines.

Main Results:

  • HR-Mamba achieved an F1-score of 83.93% on dense urban imagery, an improvement of 2.98% over HRNet.
  • The network demonstrated enhanced detail fidelity and global consistency in building extraction.
  • Post-processing pipeline successfully generated regular, smooth, and engineering-ready building outlines.

Conclusions:

  • HR-Mamba offers a robust solution for high-resolution building extraction in challenging urban landscapes.
  • The proposed method effectively balances detail preservation and global context understanding.
  • HR-Mamba provides a generalizable approach for remote sensing applications requiring precise building footprint delineation.