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Method for building segmentation and extraction from high-resolution remote sensing images based on improved

Fangzhe Chang1,2, Tianyue Ma2, Dantong Wang1

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A new method, YOLOv5ds-RC, improves building segmentation in remote sensing images by refining contours. This approach enhances accuracy and speed for land use monitoring and historical change analysis.

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Area of Science:

  • Remote Sensing
  • Computer Vision
  • Geospatial Analysis

Background:

  • Remote sensing image analysis faces challenges in building segmentation, including complex urban landscapes and contour extraction difficulties.
  • Existing methods struggle with accuracy and speed, hindering applications like land use monitoring.
  • High-resolution imagery from satellites like Gaofen-2 offers potential but requires advanced processing techniques.

Purpose of the Study:

  • To develop and evaluate a novel method for high-resolution remote sensing image building segmentation and extraction.
  • To address limitations in contour accuracy and extraction speed in existing building detection algorithms.
  • To improve the efficiency and objectivity of building extraction for applications such as land use change analysis.

Main Methods:

  • Proposed YOLOv5ds-RC, a method integrating target detection, semantic segmentation, and edge optimization using the YOLOv5ds network.
  • Implemented an upsampling and convolutional layer branch from the Feature Pyramid Network (FPN) for detailed semantic segmentation.
  • Incorporated a Raster compression module for refining segmentation contours and correcting non-orthogonal image distortions.

Main Results:

  • YOLOv5ds-RC achieved superior performance over the original YOLOv5ds, with accuracy increasing from 0.81483 to 0.8849.
  • Recall improved from 0.51332 to 0.63904, and mean Average Precision (mAP) rose from 0.34922 to 0.47388.
  • The method demonstrated effective contour optimization and accurate extraction of individual buildings at scale, correcting target displacement deviations.

Conclusions:

  • YOLOv5ds-RC significantly enhances building segmentation and extraction accuracy and speed in high-resolution remote sensing imagery.
  • The refined contour extraction capabilities are crucial for detailed urban analysis and historical change detection.
  • This method provides a robust solution for fully automated rapid extraction, advancing land use change monitoring.