A Semi-Automatic Framework for Dry Beach Extraction in Tailings Ponds Using Photogrammetry and Deep Learning.

Highlights: What are the main findings? A semi-automated 3D framework is developed for extracting dry beach regions in tailings ponds, effectively capturing irregular boundaries and complex textures. DeepLabv3+ achieves superior segmentation accuracy and boundary continuity compared with SegNet and UNet. What are the implications of the main findings? The demonstrated incremental training capability enables efficient model adaptation to challenging scenarios such as snow cover and strong shadows. The proposed method provides reliable 3D dry beach point clouds for accurate dry beach length measurement and morphological change monitoring. The spatial characteristics of the dry beach in tailings ponds are critical indicators for the safety assessment of tailings dams. This study presents a method for dry beach extraction that combines deep learning-based semantic segmentation with 3D reconstruction, overcoming the limitations of 2D methods in spatial analysis. The workflow includes four steps: (1) High-resolution 3D point clouds are reconstructed from UAV images, and the projection matrix of each image is derived to link 2D pixels with 3D points. (2) AlexNet and GoogLeNet are employed to extract image features and automatically select images containing the dry beach boundary. (3) A DeepLabv3+ network is trained on manually labeled samples to perform semantic segmentation of the dry beach, with a lightweight incremental training strategy for enhanced adaptability. (4) Boundary pixels are detected and back-projected into 3D space to generate consistent point cloud boundaries. The method was validated on two-phase UAV datasets from a tailings pond in Yunnan Province, China. In phase I, the model achieved high segmentation performance, with a mean Accuracy and IoU of approximately 0.95 and a BF of 0.8267. When applied to phase II without retraining, the model maintained stable performance on dam boundaries, while slight performance degradation was observed on hillside and water boundaries. The 3D back-projection converted 2D boundary pixels into 3D coordinates, enabling the extraction of dry beach point clouds and supporting reliable dry beach length monitoring and deposition morphology analysis. [ABSTRACT FROM AUTHOR]

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