<italic>E</italic>-<italic>L</italic><sub>0</sub>: Advanced Surface Segmentation of LiDAR Point Clouds in Open-Pit Mine Stepped Terrain
Ensuring the stability of open-pit mine slopes is crucial for safe and efficient mining operations. To analyze slope stability and assess geological disaster risks, LiDAR point-cloud data are widely used to create high-precision 3-D models. However, the existing segmentation methods, which are mostl...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11095335/ |
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| Summary: | Ensuring the stability of open-pit mine slopes is crucial for safe and efficient mining operations. To analyze slope stability and assess geological disaster risks, LiDAR point-cloud data are widely used to create high-precision 3-D models. However, the existing segmentation methods, which are mostly designed for urban or indoor environments, struggle with the complex terrain of open-pit mines that includes both natural variations and artificial structures, such as benches and slopes. To address this challenge, we propose a new point-cloud segmentation method based on enhanced <italic>L</italic><sub>0</sub> gradient minimization (<italic>E</italic>-<italic>L</italic><sub>0</sub>), specifically tailored for open-pit mines with benched topography. First, a normalized spatial metric is used to create a supervoxel set that preserves boundary features, thereby reducing the computation and handling density differences. Next, an adjacency graph is built, and the <italic>E</italic>-<italic>L</italic><sub>0</sub> generates initial planes. Finally, global energy optimization is applied to refine and merge these planes into a complete surface set. Given the lack of public benchmark datasets for open-pit mines, our method was tested on manually labeled data. It achieves average <italic>F</italic>1-scores of 74.7% for structural segmentation and 80.2% for boundary delineation when processing both airborne and vehicle-mounted LiDAR data. This method supports slope stability monitoring, 3-D reconstruction, and estimating quantities for earthwork in open-pit mining. |
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| ISSN: | 1939-1404 2151-1535 |