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Course:EOSC311/2026/The Impact of Key Computing Advancements on Resource Extraction

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Summary

Resource extraction has historically relied on human labour, mechanical tools, and engineering innovations to locate and recover natural resources. During the Industrial Revolution, steam-powered machinery and later electrical technologies increased the scale and efficiency of extraction activities. The introduction of computing technologies during the twentieth century further transformed the industry.

Beginning in the mid-twentieth century, resource companies increasingly adopted computers to manage the growing complexity of exploration and extraction. Research has shown that the oil industry contributed to the development of early computing technologies by creating demand for advanced modelling, data processing, and process-control systems required for geological analysis and drilling operations[1]. During the same period, mining and metallurgical industries began implementing computerized process-control systems to improve production management and operational efficiency[2].

Today, resource extraction relies on a wide range of digital technologies, including geographic information systems (GIS), Global Positioning Systems (GPS), Internet of Things (IoT) sensors, artificial intelligence (AI), robotics, and large-scale data analytics. These technologies are used to locate resource deposits, monitor equipment, optimize operations, and support workplace safety.

Connection to EOSC 311

Modern resource extraction efforts utilize a variety of machines and software to make the extraction processes safer, faster, and more efficient. Our group chose to research how different computing technology advancements have impacted the resource extraction industry, as we are majoring in Computer Science.

Module 1 introduces tools such as LiDAR and how they might be used by geologists; Module 2 goes into more depth about the resource needs of modern computing technology as well as the evolving process of exploration and mining.

GIS and Spatial Analysis

GIS and its history

Geographic Information System (GIS) is designed to capture, store, manipulate, analyze, manage and present data related to positions on Earth’s surface. GIS can use a variety of information such as locations (consisting of address, latitude or longitude), data on people (such as population and income), or landscape data (such as vegetation and soil)[3]. It allows researchers to visualize the relationships between the “what” and the “where” by comparing the locations of different things to discover how they are related[4].

In the 1960s, major technological advancements occurred that lead to early GIS creation. This included the ability to output map graphics using line printers, advances in data storage, and the processing power of mainframe computers[4]. Data input could be used to record coordinates and then perform calculations with them. GIS was then adopted more into the mainstream after 1990 as computers had gotten cheaper and more powerful.[4]

How GIS works

How GIS is used to reconsutrct a proglacial lake

There are 5 main components of GIS that allow it to handle geographic data effectively which is comprised of hardware, software, data, people and procedures. GIS operates by organizing information in layers which are then stacked on top of each other[5]. As seen in the figure below, each layer contains a different set or type of information, but they are all related based on the location. They must also be aligned to the same scale so that they fit together. GIS allows these layers to viewed together or separately.

What is Spatial Analysis

Spatial Analysis is a method that can be used to study geographic patterns, relationships and trends found in data. It goes one step further than just simply viewing the information on the map by implementing special spatial analysis techniques. Some of these common techniques are Buffer analysis, Network analysis, Hotspot analysis, Topology analysis and Predictive modelling[5]. A unique feature of spatial analysis is its flexibility, with many different numbers of layers being able to matched to get different results.

Applications of GIS and Spatial Analysis

Spatial Analysis and GIS are both widely used in our everyday lives and its applications can be found everywhere. Examples for GIS usage range from scientists using it to track changes in ice coverage in polar regions over time, geologists studying earthquake faults[3] or urban planners using it to map out all of the highways in a city. Spatial Analysis gives researchers an even deeper analysis on top of GIS data and can be used to capture harder to discover insights.  Spatial analysis can also be used to determine the shortest route to reduce travel time or predict where failures may happen based on historical patterns[5]. Working together, GIS and spatial analysis are able to complement each other perfectly, with GIS providing a strong foundation of organized data and spatial analysis using it to generate interesting insights and identify patterns, which GIS can then map out to visualize.

LiDAR and Remote Sensing

Remote sensing technologies have existed for many decades and have been used by humans to measure distances and map surfaces. Sensing technologies use a variety of different approaches, such as sound [6], radio, or light waves [7].

One of the most well known remote sensing technologies is LiDAR, short for “Light Detection and Ranging”[8]. Ever since the first LiDAR machine prototype was built in 1961 by researchers at the Hughes Research Laboratory, LiDAR sensing based technology has caused changes and improvements to many industries, including the mining industry.

How Does LiDAR Work?

LiDAR systems send out rapid light pulses using a laser. The light continues traveling until it is reflected upon contact with an object (or surface) and returns to a sensor. LiDAR systems are then able to calculate the position of the object/surface using the travel time, alongside using the intensity of the returned light for information about the type of surface it was reflected off of[9].

LiDAR point cloud. LiDAR systems use the travel time and intensity of reflected light pulses to calculate positions and generate 3D point clouds.

After collecting many distance and intensity readings, LiDAR systems can generate highly detailed 3D environment maps (called point clouds) [10].

LiDAR Applications

LiDAR technology is used by many mining companies to map terrain, monitor slope stability[11], and help autonomous vehicles navigate[12]. As a result, various mine-site jobs have become safer and more productive overall.

Terrain Mapping Efficiency

Traditionally, mine-sites would be gradually mapped by trained survey workers. This process was slow and often did not allow for complete mappings, as surveyors would be unable to access some mine-site locations due to safety risks. This is no longer always the case, however, as mobile LiDAR scanners allow regular mine-workers to assist with the terrain mapping process (with minimal required training). This allows for more complete mine-site mappings (as regular workers can take scans in some locations blocked to surveyors for safety reasons) and cuts the time required to conduct terrain scans by a significant amount. [13]

Mine-Site Safety Improvements

LiDAR is also used by various systems and devices that make mine-site work safer. For example, some mine-stability monitoring systems use LiDAR to scan mine-site slopes and detect early signs of instability[14]. Additionally, LiDAR-based survey-drones are used at many mine-sites to monitor mine-site conditions and can also collect data on areas too dangerous for regular human workers [15].

Autonomous Machines

With advances in robotics, the mining industry has also been able to automate a variety of mine-site tasks that traditionally required on-site human workers. Some of these technologies (such as driverless vehicles and autonomous drilling robots) can operate completely independently, though many still require a remote human operator.

Nutrien's logo. Nutrien operates multiple mines in Canada [16]

Many mining companies aim to increase the amount of automation at mine-sites, as they often boost productivity, reduce costs [17], and can handle dangerous tasks. For example, a Canadian company (Nutrien), recently reported that they had been developing remotely operated mining robots and had been able to mine an entire section of a mine without a single human worker physically present [18].

Autonomous Haul Trucks & Autonomous Haulage Systems

An autonomous haul truck. Autonomous haul trucks do not require a human driver and can stay in operation for long continuous periods of time.

Autonomous Haul Trucks are a type of driverless vehicle used by mining companies to transport materials to extraction plants[19]. Autonomous haul trucks use Autonomous Haulage System (AHS) software (that handles collision detection and path planning) to navigate mine sites without remote human control[20]. Autonomous trucks also require minimal remote human oversight/monitoring and can safely stay operational nearly at all times[21].

As autonomous trucks do not require a human driver, mining companies are able to eliminate the risk of human error (or reckless behaviour) impacting material transportation [17]. Additionally, mining companies have observed mental health improvements in their human workers, as autonomous haul trucks can be monitored remotely and thus allows for an increase in offsite work (in nicer locations)[17].

How Resource Extraction Enables New Technologies

As new technologies are being developed everyday, resource extraction is not only an aid in these innovations but instead a mandatory foundation. Many new and powerful technologies we rely on must be powered by some sort of resource and critical minerals are often in the mix.

Importance of Resources in Technologies

There are a diverse range of critical minerals that are essential in modern computing and the development of Artificial Intelligence (AI). These raw minerals like Lithium, Cobalt, Silicon and others are essential in the building of batteries, high-performance magnets and semi conductors that we need to develop and power new technologies such as electric vehicles, smartphones, wind turbines and AI data centers[22]. Within these raw materials, Silicon is responsible for forming the basis for microchips, cobalt enhances memory and logic devices, and Lithium powers batteries that support portable storage devices and batteries. Although they all have different uses, they play key roles in ensuring that new technologies can be developed[22].

Urban Mine

Another way that resource extraction has paved the way for new technologies is the recycling of old technologies into critical minerals that we can reuse, which is interesting because it seems to be the opposite direction. However, this is pivotal way for waste to be safely removed and to combat increased critical mineral demands. These old technologies in question are E-waste such as old phones, batteries and circuit boards[23]. Some examples of Critical resources that can be extracted from E-Waste include copper, aluminum, palladium, cobalt, lithium, tantalum, silver and gold, with approximately 300 grams of gold in a tonne of mobile phones[24]. From old technology, we can use newly recycled critical minerals and resources to develop new technology.

Environmental and Social Impacts

Environmental Footprint

Computing technologies have improved the efficiency of resource extraction but also create environmental impacts of their own[25]. AI tools can be used to inform environmental policies and minimize the impact of extraction operations on surrounding ecosystems[26]; however, modern computing infrastructure requires substantial amounts of electricity, water, and raw materials. Data centres, which support cloud computing and artificial intelligence applications, consume significant amounts of energy and are expected to account for an increasing share of global electricity demand in coming years[27].

Women extracting cobalt by hand in a mine in the Democratic Republic of the Congo
Workers in the Democratic Republic of the Congo extracting cobalt by hand. Cobalt is one of the most valuable minerals in computers; though technology alleviates the need for human labour in the West, it creates a heavy demand in developing nations[17].

The production of computing hardware also depends on the extraction of critical minerals such as lithium, cobalt, copper, tantalum, and rare earth elements[28]. As a result, digital technologies remain closely connected to resource extraction through their material requirements.

Social and Workforce Impacts

The adoption of computing technologies has changed the nature of work within the resource sector. Historically, many extraction activities relied on large numbers of workers performing physically demanding and potentially hazardous tasks. Modern operations increasingly use automation, remote-control systems, and centralized operations centres to manage equipment and processes[2].

These developments have reduced the need for workers to perform dangerous on-site tasks while increasing demand for technical skills in areas such as engineering, information technology, robotics, and data analysis. Research suggests that automation is reshaping workforce requirements by shifting workers into monitoring, maintenance, and operational support roles rather than eliminating labour entirely[29].

However, critics and researchers have noted that demand for minerals used in computing technologies can place social and environmental burdens on communities in producing regions. Concerns include labor conditions, workplace safety, environmental contamination, and the unequal distribution of economic benefits across global supply chains. Despite supplying materials essential to modern computing technologies, mining communities do not always share equally in the benefits generated by these industries[17].

Future Trends

Emerging Developments

Several emerging technologies continue to influence resource extraction.

AI-assisted exploration uses machine-learning algorithms to analyze geological, geophysical, and satellite datasets in order to identify potential resource deposits more efficiently[26].

Autonomous vehicles and equipment are increasingly used in mining operations. Autonomous haul trucks, drilling systems, and robotic machinery can operate continuously while reducing risks associated with human exposure to hazardous environments[30].

Digital twins are virtual models of physical assets or operations that are updated using real-time sensor data. Resource companies use digital twins to monitor equipment performance, optimize production processes, and support operational planning[31].

Internet of Things (IoT) systems allow companies to collect continuous operational data from equipment and infrastructure. These systems support predictive maintenance, environmental monitoring, and operational decision-making[32].

Potential Future Developments

Future developments may further expand the role of computing in resource extraction. Researchers and industry organizations have identified several areas of potential growth, including highly automated extraction operations, improved AI systems for resource discovery, and advanced robotics designed for use in extreme environments such as deep-sea mineral deposits[33].

Computational modelling may also contribute to the development of carbon capture and storage systems by helping identify suitable geological formations for long-term carbon sequestration[34]. In addition, AI-driven energy management systems may support greater integration of renewable energy sources into extraction operations, potentially reducing greenhouse gas emissions associated with resource production[27].

Conclusion

Computing technology has significantly transformed the resource extraction industry through improvements in efficiency, safety, automation, and data analysis. At the same time, the growth of computing technologies has increased demand for energy, water, and critical minerals while raising important social and environmental considerations. As computing continues to evolve, balancing technological innovation with sustainable resource management, human safety, and equitable economic outcomes will remain an important challenge for industry, governments, and researchers.

References

  1. ↑ Mody, Cyrus C. M. (22 February 2022). "Spillovers from Oil Firms to U.S. Computing and Semiconductor Manufacturing: Smudging State–Industry Distinctions and Retelling Conventional Narratives". Enterprise & Society. 24 (3) – via Cambridge University Press.
  2. ↑ 2.0 2.1 Aylen, Jonathan (2004). "Megabytes for metals: development of computer applications in the iron and steel industry". Ironmaking & Steelmaking – via Academia.edu.
  3. ↑ 3.0 3.1 "GIS (Geographic Information System)". Society.
  4. ↑ 4.0 4.1 4.2 Steenson, Rachel (25 April 2019). "The history of Geographic Information Systems (GIS)".
  5. ↑ 5.0 5.1 5.2 Noordam, gijsbert (11/06/2025). "What is the difference between GIS and spatial analysis?". Check date values in: |date= (help)
  6. ↑ Dinneen, James (May 19, 2020). "Reginald Fessenden and the Invention of Sonar". Science History Institue. Retrieved June 12, 2026.
  7. ↑ "The Evolution of LiDAR". Flyguys. June 28, 2022. Retrieved June 11, 2026. |first= missing |last= (help)
  8. ↑ "What is lidar?". National Ocean Service. Retrieved June 14, 2026. |first= missing |last= (help)
  9. ↑ "LiDAR Intensity: What is it and What are it's applications?". Retrieved June 14, 2026. |first= missing |last= (help)
  10. ↑ "Introduction to Lidar". Retrieved June 14, 2026. |first= missing |last= (help)
  11. ↑ Smith, Brett (Feb 13, 2025). "How is Remote Sensing Used in Mining?". AZO Mining.
  12. ↑ "Caterpillar signs LiDAR deal for mining truck autonomy with MicroVision". International Mining. Jun 12, 2026. |first= missing |last= (help)
  13. ↑ "Improving mine efficiency and safety with mobile LiDAR". Faro Creaform. Retrieved June 9, 2026. |first= missing |last= (help)
  14. ↑ "LiDAR Slope Monitoring: Can LiDAR Detect Land Slippage?". GeoAI. Retrieved June 15, 2026. |first= missing |last= (help)
  15. ↑ "Using Drones to Assess Inaccessible or Dangerous Areas in Mining". CANDRONE. Retrieved June 9, 2026. |first= missing |last= (help)
  16. ↑ "Locations". Nutrien. |first= missing |last= (help)
  17. ↑ 17.0 17.1 17.2 17.3 17.4 "Autonomous mining systems offer safety and cost savings advantages during COVID-19". Komatsu. Aug 9, 2020. Cite error: Invalid <ref> tag; name ":3" defined multiple times with different content
  18. ↑ Stephenson, Amanda (Jun 25, 2023). "Mining companies betting on autonomous technology to make dangerous jobs safer". CBC News.
  19. ↑ "The Future of Canadian Mining is Autonomous — and Suncor is Leading the Way". Suncor. June 30, 2025. |first= missing |last= (help)
  20. ↑ "Autonomous Haulage System". Komatsu. |first= missing |last= (help)
  21. ↑ "Autonomous Haulage System（AHS)". Hitachi. |first= missing |last= (help)
  22. ↑ 22.0 22.1 "Critical Minerals in AI and Digital Technologies".
  23. ↑ Walter, Neetika (02/24/2026). "Urban mine': Collaborative method unlocks critical minerals from electronic waste". INTERESTINGENGINEERING. Check date values in: |date= (help)
  24. ↑ "E-Waste Becomes a Strategic Resource as Global Competition for Critical Materials Intensifies". 02/05/2026. Check date values in: |date= (help)
  25. ↑ OECD Green Growth Studies (25 September 2019). "Mining and Green Growth in the EECCA Region". OECD Publishing. Retrieved June 15 2026. Check date values in: |access-date= (help)
  26. ↑ 26.0 26.1 Gordon, Janice M. "Artificial Intelligence Strategy for the U.S. Geological Survey". U.S. Geological Survey Circular. 1562. Retrieved June 15 2026 – via U.S. Geological Survey Publications. Check date values in: |access-date= (help)CS1 maint: display-authors (link)
  27. ↑ 27.0 27.1 "Energy and AI". International Energy Agency. 10 April 2025.
  28. ↑ "Key Minerals in Data Centers Infographic". U.S. Geological Survey. July 2025. Retrieved June 15 2026. Check date values in: |access-date= (help)
  29. ↑ Fernandez-Stark, Karina; Couto, Vivian; Bamber, Penny (January 28, 2019). "Industry 4.0 in Developing Countries: The Mine of the Future and the Role of Women" (PDF). WBG-WTO Global Report on Trade and Gender – via World Bank.
  30. ↑ Obosu, Mabel; Frimpong, Samuel (September 2025). "Advances in automation and robotics: The state of the emerging future mining industry". Journal of Safety and Sustainability. 2 (3): 181–194 – via Elsevier Science Direct.
  31. ↑ Cranford, Rachel (20 July 2023). "Conceptual application of digital twins to meet ESG targets in the mining industry". Frontiers in Industrial Engineering. 1 – via Frontiers.
  32. ↑ Molaei, Fatemeh; et al. (16 September 2020). "A Comprehensive Review on Internet of Things (IoT) and its Implications in the Mining Industry" (PDF). American Journal of Engineering and Applied Sciences. 13 (3): 499–515 – via HAL Open Science.
  33. ↑ Zhang, Qi; et al. (March 2025). "Technology and equipment of deep-sea mining: State of the art and perspectives". Earth Energy Science. 1 (1): 65–84 – via Elsevier Science Direct.
  34. ↑ Sun, Zhuang; et al. (15 April 2025). "A comprehensive review of CO2 geological storage projects with regulatory requirements of computational modeling". Fuel. 386 – via Elsevier Science Direct.


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