MODERN METHODS IN GEODESY AND MINE SURVEYING: INTEGRATING AI TECHNOLOGIES

ABSTRACT

Modern AI-based methods improve accuracy, efficiency and safety in geodesy and surveying, replacing traditional manual measurements with automation.

Keywords: geodesy; AI; mine surveying.

1. Introduction

Geodesy and mine surveying are fundamental disciplines that provide accurate spatial data for urban planning, environmental monitoring, and resource extraction. While geodesy focuses on measuring the Earth's shape, orientation, and gravitational field, mine surveying applies these principles to map underground and surface mining sites. Historically, both fields relied on labor-intensive and time-consuming methods. However, the integration of AI has revolutionized these practices, enabling real-time data processing, automation, and enhanced decision-making.

2. Traditional Methods and Their Limitations

Traditional surveying and geodesy techniques have relied on tools such as theodolites, total stations, and GNSS receivers. In mine surveying, these instruments were used for tunnel mapping, volume calculations, and slope monitoring. However, these methods present several challenges:

• Time-Consuming Data Collection: Manual surveys require significant human effort.
• Limited Accuracy in Harsh Environments: Underground mining conditions and rough terrains affect measurement precision.
• Integration Challenges: Combining data from multiple sources (e.g., satellite imagery, LiDAR, and ground surveys) is often complex.

3. AI-Driven Innovations in Geodesy and Mine Surveying

The application of AI is addressing these limitations by enhancing data acquisition, processing, and predictive modeling.

3.1 Automated Data Collection and Integration

• Drones and LiDAR Mapping: UAVs equipped with high-resolution cameras and LiDAR sensors enable rapid aerial and underground mapping. AI algorithms process 3D point clouds to create highly accurate digital terrain models.
• Satellite-Based Geodesy and InSAR: AI-driven InSAR analysis allows for precise deformation monitoring of mining areas and urban environments.
• Autonomous Mine Surveying Systems: AI-powered robots and automated total stations improve tunnel mapping and excavation planning with minimal human intervention.

3.2 Advanced Data Processing and Predictive Analytics

• Real-Time Deformation Monitoring: AI detects structural weaknesses in mines and geotechnical sites, helping prevent collapses.
• Machine Learning for Volume Estimation: AI enhances accuracy in calculating extracted material volumes and optimizing resource management.
• GIS Integration: AI-driven GIS platforms combine multisensor data for comprehensive land and mine planning.

3.3 Enhancing Safety and Efficiency

• Automated Hazard Detection: AI-based image recognition detects cracks, fractures, and unstable rock formations.
• Remote Monitoring of Mining Operations: AI allows for real-time assessment of underground conditions, reducing risks for workers.
• Optimization of Land and Resource Management: AI-driven decision-making improves land use planning and environmental impact assessments.

4. Applications and Case Studies

AI-powered geodesy and mine surveying are transforming various industries:

• Urban and Infrastructure Development: AI improves land survey accuracy for road construction, zoning, and smart city planning.
• Mining and Natural Resource Management: AI enhances safety and efficiency in mining operations by automating hazard detection and optimizing excavation processes.
• Environmental and Disaster Monitoring: AI-based geospatial analysis predicts natural disasters and monitors deforestation and erosion.

5. Challenges and Future Directions

Despite its advantages, AI adoption in geodesy and mine surveying faces several challenges:

• Data Standardization: AI models require high-quality, standardized geospatial data.
• Integration with Legacy Systems: Many mining and surveying firms operate with outdated technologies that require costly upgrades.
• Ethical and Security Concerns: AI-driven automation must consider data privacy, cybersecurity, and ethical implications in resource management.

Future developments in edge computing, augmented reality (AR) visualization, and AI-driven autonomous surveying systems promise to further enhance the accuracy, efficiency, and safety of geodesy and mine surveying.

6. Conclusion

The integration of AI in geodesy and mine surveying is revolutionizing the field by improving accuracy, efficiency, and safety. From automated drones and machine learning models to real-time monitoring and predictive analytics, AI is reshaping how spatial data is collected, processed, and utilized. While challenges remain, continuous advancements in AI and digital technologies will drive the future of geospatial sciences and mining operations, making them smarter, safer, and more efficient.

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