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August 16, 2025

Intelligent recognition and vectorization automatic extraction technology of mining area buildings based on high-resolution remote sensing images

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The research develops a method for building extraction using transfer learning and image fusion, enhancing accuracy in challenging environments.

Building extraction accuracy reached 83.12% using a combination of classification techniques, including support vector machine and random forest models.
The study employed multi-scale image fusion and image enhancement, resulting in improved detail resolution and clearer building boundaries.
Transfer learning significantly reduced the need for large annotated datasets, simplifying the process of recognizing buildings in complex environments.
Future enhancements may integrate deep learning models like U-Net and DenseNet to further boost extraction capabilities and applicability.

Guo et al. (2025) studied this question.

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The research develops a method for building extraction using transfer learning and image fusion, enhancing accuracy in challenging environments.