用无人机+激光雷达+深度学习，实现矿区高精度空间信息自动获取
UAV Object Detection and Positioning in a Mining Industrial Metaverse with Custom Geo-Referenced Data
- 无人机采集数据，结合激光雷达构建三维地形模型
- 通过深度学习实现物体定位，输出可接入数字孪生平台的空间数据
- 系统模块化设计，适合工业现场部署，支持未来实时应用
采矿业正越来越多采用数字化工具提升运营效率、安全性和数据驱动决策能力。核心挑战之一是可靠获取高分辨率、地理参考的空间信息，以支持开采规划和现场监控等关键任务。本文提出一种集成系统架构，融合无人机传感、激光雷达地形建模与基于深度学习的物体检测技术，为露天矿环境生成空间精确的信息。该流程包括地理参考、三维重建和物体定位，生成可整合至工业数字孪生平台的结构化空间输出。相比传统静态测绘方法，本系统具有更高覆盖范围和自动化潜力，组件模块化，适用于真实工业场景部署。当前版本为飞行后批量处理模式，但已为实时扩展奠定基础。该系统推动了采矿领域人工智能增强遥感的发展，展示了可扩展且经实地验证的地理空间数据工作流，有助于提升态势感知与基础设施安全性。
原文摘要 · Abstract (English)
The mining sector increasingly adopts digital tools to improve operational efficiency, safety, and data-driven decision-making. One of the key challenges remains the reliable acquisition of high-resolution, geo-referenced spatial information to support core activities such as extraction planning and on-site monitoring. This work presents an integrated system architecture that combines UAV-based sensing, LiDAR terrain modeling, and deep learning-based object detection to generate spatially accurate information for open-pit mining environments. The proposed pipeline includes geo-referencing, 3D reconstruction, and object localization, enabling structured spatial outputs to be integrated into an industrial digital twin platform. Unlike traditional static surveying methods, the system offers higher coverage and automation potential, with modular components suitable for deployment in real-world industrial contexts. While the current implementation operates in post-flight batch mode, it lays the foundation for real-time extensions. The system contributes to the development of AI-enhanced remote sensing in mining by demonstrating a scalable and field-validated geospatial data workflow that supports situational awareness and infrastructure safety.
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