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May 17, 2026SensorsOpen Access

Global–Local Feature Fusion Network for Remote Sensing Image Change Detection in Open-Pit Mining Areas

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Authors

ZZZ-Y ZhengUniversity of Science and Technology of ChinaJYJie YangGeneral CardiologyGLGuanghui LvXinjiang University

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Implication

Randomized trial demonstrates effective change detection in open-pit mining areas, indicating improved monitoring capabilities.

Key Points

  • The aim is to enhance the detection of changes in open-pit mining areas using advanced remote sensing techniques.
  • Developed GLMECD-Net, a Global-Local Multi-scale Cross-fusion Enhanced Change Detection Network.
  • Utilized a Siamese encoder for hierarchical bi-temporal feature extraction.
  • Implemented multi-scale feature aggregation and cross-temporal feature fusion for improved change representation.
  • Achieved 71.66% Precision and 83.78% Overall Accuracy.
  • Attained 77.53% F1-score and 53.82% Intersection over Union (IoU).
  • Demonstrated effective performance in detecting complex and subtle changes.

Cite This Study

Zheng et al. (2026) studied this question.

synapsesocial.com/papers/6a095b5d7880e6d24efe10c1https://doi.org/10.3390/s26103128
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