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Updated: Mar 28, 2026

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Three-dimensional spatial representation method for semantic boundaries in digital outcrop models.

Zhicheng Dong1, Hongjun Zhang2, Yingwei Qu1

  • 1Liaoning Finance and Trade College, Xingcheng, Huludao, Liaoning Province, China.

Scientific Reports
|March 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a 3D geological modeling method for precise outcrop information extraction using AI-powered semantic segmentation and spatial projection. The approach significantly improves boundary recognition and accuracy for digital twin development.

Keywords:
Cesium visualizationGeological outcropSemantic segmentationSpatial mappingU-Net

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Area of Science:

  • Geoscience
  • Computer Vision
  • Photogrammetry

Background:

  • Automated geological outcrop analysis is crucial for understanding Earth's history.
  • Current methods struggle with spatial accuracy and complex geological structure extraction.
  • High-resolution 3D modeling requires precise semantic segmentation of outcrop features.

Purpose of the Study:

  • To develop an automated 3D geological modeling method for enhanced spatial accuracy.
  • To improve multi-class semantic boundary representation of geological outcrops.
  • To create a digital twin of geological structures for further analysis.

Main Methods:

  • Acquired centimeter-scale outcrop images using UAV-based oblique photogrammetry.
  • Developed a modified U-Net model with self-attention and boundary-weighted IoU loss for semantic segmentation.
  • Implemented a spatially-constrained projection strategy using Z-buffer depth testing and weighted majority voting for 2D to 3D mapping.
  • Performed high-precision 3D reconstruction and constructed a multi-scale semantic visualization platform.

Main Results:

  • Achieved 78.64% Boundary F1-Score for boundary-aware semantic segmentation.
  • Limited the mean projection error to within 3.2 cm.
  • Demonstrated superior performance compared to existing mainstream approaches in geological structure recognition.

Conclusions:

  • The proposed method effectively enhances automation and spatial accuracy in geological outcrop information extraction.
  • This research offers a robust pathway for automated recognition of complex geological structures.
  • The developed techniques facilitate the creation of advanced digital twin models for geological applications.