基于多技术协同的矿山生态修复监管研究

Research on Mine Ecological Restoration Supervision Based on Multi-Technology Integration

摘要: 针对传统露天矿山生态修复监管存在手段单一、人工干预量大、量化精度低、动态监管能力不足等问题，以露天损毁矿区为研究对象，构建深度学习与三维测绘技术协同的矿山生态修复智能化监管体系。基于U-Net语义分割框架，嵌入多类ResNet骨干网络完成模型对比试验，优选U-Net + ResNet34模型实现矿区土地损毁区域高精度智能识别，并通过人工解译检核验证模型的可靠性，有效提升矿区损毁信息提取的自动化与精准化水平；依托无人机倾斜摄影与机载LiDAR融合建模技术，构建矿区多时序高精度三维实景模型，兼顾地形几何精度与场景可视化效果；基于时序三维模型差分算法，实现矿区地形重塑效果、分区土方填挖量的定量化核算与施工动态监管，明晰不同修复单元施工强度的空间分异规律。结果表明，该监管体系在损毁区域识别精度、地形变化量化分析及施工进度动态追踪等方面均表现出良好适用性，能够为矿山生态修复全流程监管提供高效、精准的技术手段，有效提升矿山生态修复监管的智能化与精细化水平。

Abstract: To address the limitations of traditional open-pit mine ecological restoration supervision—such as single monitoring methods, heavy manual intervention, low quantification accuracy, and insufficient dynamic monitoring capacity—this study takes damaged open-pit mining areas as research subjects and constructs an intelligent supervision system for mine ecological restoration by integrating deep learning with three-dimensional surveying technologies. Based on the U-Net semantic segmentation framework, multiple ResNet backbone networks are embedded to conduct comparative model experiments, and the U-Net + ResNet34 model is selected for its superior performance in accurately identifying damaged land areas within mining zones. The reliability of the model is verified through manual interpretation and validation, significantly enhancing the automation and precision of damage information extraction. By leveraging integrated modeling techniques combining drone oblique photogrammetry and airborne LiDAR, high-precision multi-temporal 3D realistic models of mining areas are established, ensuring both geometric accuracy of terrain and visual realism. Using a time-series 3D model differencing algorithm, quantitative assessments of terrain reshaping effects and earthwork volumes (cutting and filling) across different zones are achieved, enabling dynamic construction supervision and revealing spatial differentiation patterns in construction intensity among various restoration units. Results demonstrate that this supervision system performs well in terms of damaged area identification accuracy, quantitative terrain change analysis, and dynamic progress tracking, providing efficient and precise technical support throughout the entire mine ecological restoration process, thereby significantly improving the intelligence and refinement level of mine ecological restoration supervision.