Mining is critical for a steady supply of energy resources and rare earth metals for technological advancements. Yet, it’s also a tough business with enduring operating conditions, safety hazards, and stringent regulations.

Many operating processes require aerial visibility – drones have become indispensable for land surveying, risk assessments, stockpile management, and an array of other use cases.

3 Use Cases of Drones in The Mining Industry 

Since 2016, 70% of large mining companies have gone from using drones for fringe cases to fully integrating the technology into surveying, engineering, and monitoring workflows.

Learn how leaders are using drones to optimize operations, cut costs, and increase employee safety.

Streamlined Land Surveying

Mines need land surveying services constantly and in large quantities. Aerial monitoring ensures safe on-site operations, resource assessments, and expansion planning.

Traditional land survey methods like triangulation, tachymetry, chane and tape surveying are time-consuming and require a large on-foot workforce. Surveying personnel at an active mine can interfere with operating equipment, leading to costly downtime. Larger projects like pit-to-port road construction or new mineral exploitation require coverage of large distances. 

Drones are a more time- and cost-effective alternative to conventional land surveying methods. The best fixed-wing and quadcopters with a gas-electric powertrain can stay in the sky for several hours, covering 250-1000+ acres in one flight.

The selection of land-surveying drone payloads available is impressive: HD RGB, oblique, thermal, and multispectral cameras, LiDAR sensors with centimeter-level positioning accuracy, magnetometers, and photogrammetric cameras. Thanks to advances in aerial autonomous navigation, the best drones for mining now have automated data capture modes and can generate 3D environment maps for better situational awareness.

Roughly 83% of mining companies use drones for mapping and surveying, including the Baorixile Open Pit Mine. With a 50 square kilometer mining license area and 640 million tons of coal reserves, the mine is crucial for the local market. However, harsh environmental conditions — snow, sub-zero temperatures, icing — make land surveying missions challenging. Likewise, naturally occurring electromagnetic interference (EMI) hampered drone navigation, causing GPS signal loss and IMU instability. So instead of sending personnel, the operators switched to using Jouav CW-25E UAV with LiDAR technology. 

The drone collected 125 times more data points per square meter than conventional RTK surveying, with under 5 cm accuracy. Data acquisition times were reduced from 13 days to 2 — a 6.5X increase in efficiency. Personnel no longer had to endure the harsh climate, controlling the drone from a parked vehicle.

Stockpile Management

Stockpile management is another labor-intensive task in mining operations. Accurate volume and mass calculations are challenging due to extreme heights and irregular shapes (which also change frequently). However, ensuring structural stability is crucial to prevent accidents. Stockpile collapses and nearly avoided accidents trigger regulatory investigations and fines.

With drones in mining, companies can produce aerial terrain models of their inventory and keep track of stockpile changes over time to ensure safe and effective operations.  Ferrexpo Yeristovo Mine regularly employs drones to conduct land surveys, create up-to-date stockpile maps, and minimize risk to personnel. 

DroneUA helped Yeristovo mine operators create a 3D model of an 82-hectare / 202-acre open pit, collecting data from 410 meters/1345 feet below ground. The team collected over 1,000 data points for 2D/3D models. 

The second major project involved mapping an ore stockpile. The team created pre-planned navigation routes to automate data collection and ensure high data fidelity. Stockpiles occupy large areas with uneven terrain, making hand-held tools less effective. Drones, equipped with photogrammetric cameras, allow greater precision and keep personnel out of dangerous areas. In both cases, drone-led data collection took 90% less time than traditional methods.

Mineral Exploration 

Drones in mining provide a new vantage point for exploring new mineral sites, as well as resource evaluation and subsequent excavation planning. Thanks to advanced onboard sensing technologies and specialized payloads (e.g., an echosounder, metal detector, magnetometer, etc), drones can be used to perform geological mapping missions at faster speeds. For example, identify ore bodies’ spatial distribution or detect tramp metal in stockpiles to prevent costly crusher outages during the excavation phase. 

Yet, drone deployments on mining sites can be challenging due to high EMI levels. Some minerals (e.g.,  magnetite or pyrrhotite), as well as mining equipment like proximity sensors or high-voltage power cables, can interfere with the navigation systems. Bavovna developed an AI-driven hybrid navigation kit for VTOL, Fixed Wing, Multirotor, or Quad to safely cruise in high EMI environments. With Bavovna’s AI navigation kit drones can maintain ​​99% positional accuracy in high EMI/GPS-denied environments and reliably follow the original flight path despite the interferences. The navigation unit is housed in an EMI-protected box, attenuating interferences at 92dB per mm. 

Unlike computer vision-based systems, which require substantial computing resources, Bavovna’s solution relies only on device sensor data, streamed into a lightweight AI model for fully autonomous operations.

Underground tunnel mapping is another labor-intensive task at new mining sites. Irregular tunnel shapes and confined spaces complicate equipment placement and safety. Physical obstructions and geological interferences render traditional surveying methods ineffective. High dust and low lighting limit the usage of optical surveying tools.

Confined space drones have emerged to address these challenges. Featuring a rugged, caged design and advanced obstacle detection, the best indoor drones can cruise through narrow passages and access restricted areas to collect mapping data. Elios 3 from Flyability uses LiDAR and computer vision technologies to generate real-time 3D-environment maps for piloting in GPS-denied environments.

Similar to above-ground land surveying, indoor drones create operational efficiencies and savings. At Glencore Kidd Mine in Ontario operators used Elios 3 to eliminate time-consuming drilling and expensive equipment movement. Within 15 minutes, the team inspects flagged areas and gains video footage and 3D maps of underground conditions.

Conclusion

The usage of drones in mining has grown at a massive clip over the years and for some good reasons. Aerial surveillance capabilities, coupled with a great array of payloads, help miners conduct faster site assessments, survey underground tunnels, and improve asset management lifecycles. 

Thanks to advances in onboard sensing capabilities and new navigation technologies like Bavovna’s kit, drone flights have also become much safer even in GNSS-denied and tough environmental conditions, paving the way for even more deployment scenarios. 

Global Navigation Satellite Systems (GNSS) systems such as GPS have been a staple technology in land, maritime, and aerial navigation for decades. Super useful? Absolutely. But, GNSS has its fair share of downsides too.

Weather, buildings, natural obstacles, and some electronic systems cause inevitable signal degradation and latency in signal acquisition. Heavy reliance on a network of satellites and ground stations, attractive targets for physical or cyber-attacks, also raises rightful security concerns. For UAVs and ROVs, signal spoofing, jamming, and other forms of electromagnetic warfare (EW) are also a problem. 

For many mobility scenarios signal loss or high latency is unacceptable, even more so if we’re to advance to full autonomy. But if not GNSS, what other technologies can provide reliable positioning and navigation data? A couple of options are on the table. 

Sensor Fusion with AI 

Every commercial UAV is equipped with a set of standard sensors, such as an accelerometer, gyroscope, compass, airflow sensor, and altimeter, for situational awareness. More advanced enterprise models may also include LiDAR distance sensors or ultrasonic sensors to detect proximity to obstacles.

Combined, these provide real-time information on the drone’s location, orientation, and movement, and a GPS/GNSS receiver tallies this with geo coordinates, which allow the drone to follow a pre-planned flight route. When GPS is down, the best models will hold position or attempt to automatically return home. 

Yet, recent advances in sensor fusion also enable autonomous navigation scenarios. Instead of the GPS signal and pilot commands, the drone can rely on data from its Inertial Measurement Unit (IMU) and AI algorithms to safely continue the route. By integrating and processing sensor data streams on the device, AI pilot models can provide precise situational awareness to drones and full autonomy. 

Bavovna Navigation Kit includes an 800gr/28 oz onboard edge devices and SaaS access to a fine-tuned AI Hybrid navigation model, pre-trained on historical data from the available sensors. Already implemented on eight UAV platforms, ranging from fixed-wing and VTOLs to multi-copters and FPVs, the system maintains an End Point Positioning Error (EPPE) of under 0.5% on complex trajectories of up to 30 km or 18.6 miles. 

Intended for dual use, Bavovna’s hardware sits in an EMI-protected case and can be further augmented with a SIGINT RF module for bypassing EW and EM obstacles. AI pilot system, not only enables more reliable autonomous navigation but also improves obstacle detection, UAV routing, and ultimately operating efficiencies. 

LiDAR-Based SLAM Systems

An alternative approach to sensor fusion is the Simultaneous Localization and Mapping (SLAM) technique. While sensor fusion primarily relies on IMU data, SLAM algorithms use camera and LiDAR data to general a real-time map and localize the piloted system in a GPS-denied environment. Opposed to cameras, 3D LiDARS provides an immediate point cloud of the environment, eliminating the latency. Data processing software has to be installed both on the flight controller and the onboard computer. 

SLAM algorithms like LOAM and Cartographer have demonstrated great performance during scientific experiments in terms of trajectory execution and landing errors. Commercially, Elios 3 from Flyability and Hovermap series from Emesent, and Scout 137 from ScoutDi rely on SLAM for indoor navigation in GNSS-denied environments (with an added bonus of advanced mapping scenarios). 

The downside of photo-SLAM, however, is high resource consumption: CPU, memory, and processing, especially for photo-realistic mapping scenarios. This, in turn, requires heavier and more expensive onboard units, which increase the system operating costs. Likewise, high resource consumption results in faster battery drainage on smaller UAVs, undercutting the precious flight time. Although some recent advancements in processing frameworks help optimize resource usage. 

AI Pilots: Navigation Guided by Magnetic Fields 

Commercial and military aircraft are also increasingly relying on AI-assisted navigation, oftentimes powered by hybrid INS/GPS signals. For security reasons, however, the military is looking to progressively minimize reliance on satellite networks.  

The US Air Force has been continuously testing different AI pilots to autonomously pilot fighter jets. Technologically, we’re already there — AI systems successfully piloted an F-16 in a dogfighting exercise over California’s Edwards Air Force Base in May 2024. But there’s always room for further improvements. 

The Air Force research team is working on a new AI navigation pilot to potentially navigate the plane using the Earth’s magnetic fields. So far, such scenarios haven’t been feasible as the aircraft generate too much electromagnetic noise, interfering with onboard magnetometer measurements.

However, a recent model, tested on C-17, showed very promising results that could lead to “tactical airdrop quality” and a potential solution to “things we can do, should we end up operating in a GPS-denied environment.“, according to Col. Garry Floyd, director of the Department of Air Force-MIT Artificial Intelligence Accelerator program.

While we may not be there yet, widespread autonomous AI piloting without GNSS is no longer a question of “if”, but rather “when”, especially for unmanned aerial vehicles.