Explore ground truth for mining
We elevate standards in mining. Our expertise and ground truth annotation platform ensure that we meet the unique demands of the mining industry.
We uncover innovation across mining
Advancing the mining sector, our dedicated expertise and automation technologies are refining operations across the board. Our ground truth platform, tailored specifically to industry needs, improves safety, resource management, and operational insight at mining sites and quarries. Partner with us to enhance safety standards and operational efficiency in the mining industry.
Ground Truth use cases within mining
From autonomous haulage systems to safety monitoring, understand.ai addresses key ground truth use cases in the mining industry.
Autonomous Haulage Systems
Ground truth annotations are used to train algorithms for autonomous haulage systems, providing accurate data on road conditions and obstacles to enhance safety and operational efficiency.
Terrain Mapping
Ground truth annotations facilitate precise terrain mapping by offering detailed, labeled data on surface features, aiding in the accurate modeling and analysis of mining environments.
Safety Monitoring
Ground truth annotations assist in safety monitoring by identifying potential hazards and ensuring compliance with safety protocols, contributing to a safer mining operation.
Terrain Exploration
Ground Truth annotations support terrain exploration by providing detailed, reliable data on land features and obstacles, helping to optimize exploration strategies and uncover valuable resources.
Reference Case: ADIS development
Explore this following reference case to gain deeper insight of what a successful collaboration with us could look like.
In this examplary reference case, we successfully assisted a mining company by providing the essential ground truth data required for the development of an Advanced Digital Inspection System (ADIS) using machine learning. The ADIS aimed to enhance safety, efficiency, and asset management within mining operations. The system relied on data from cameras, sensors, and LiDARs to monitor equipment, worker safety, and critical safety points.
The Challenge
- Harsh Conditions: The mining environment included extreme conditions, making accurate data labeling difficult due to dust, lighting variations, and rough terrain.
- Complex Scenes: The crowded nature of mining sites, with various machinery and personnel, required precise annotation to ensure the ADIS could differentiate between objects and activities.
- Automated Workflow: An efficient, automated workflow for object detection needed to be designed to streamline the production process and improve the system’s overall efficiency.
Key Facts
- Data Volume: Approximately 100 hours of video and sensor data were labeled to train the ADIS models effectively.
- Project Duration and Sensor Setup: The project spanned 5 months, including the setup of sensor arrays typical for autonomous vehicles in the mining industry, such as multi-angle cameras, LiDAR units, and environmental sensors.
Key Achievements
- Enhanced Safety: The ADIS significantly improved safety by accurately detecting potential risks.
- Operational Efficiency: The system reduced downtime by enabling early detection of equipment issues.
- Scalability: Our labeling process was scalable, allowing the ADIS to adapt to various mining sites.