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Metals & Mining

Satellite data analytics solution for risk detection via geospatial analysis

Key facts


algorithms for specific functions


alerts accuracy



Next Steps
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Top business benefits

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Physical, chemical and biological analyseis supported

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Advanced AI

Advanced AI platform spots problems so you can take action before it’s too late

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Many use cases

>10 mining use cases from tailing monitoring to methane measurement

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No software installation

Turnkey model – SaaS platform, no IT security hurdles

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Customised models can be builtAPIs and Web services enable integration with existing tools

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Constant vigilance™

Achieve a higher level of monitoring

Advanced machine learning algorithms analyse satellite and other sensor data sources to alert you to pending industrial issues.

Main features

  • Collects multi and hyperspectral data from a range of third-party sources, including satellites, drones, and aircraft

  • Conducts physical analysis (e.g. encroachment, land movement, change detection), chemical analysis (e.g. acid mine drainage), and biological analysis (e.g. vegetation management)

  • Uses AI algorithms to isolate spectral signatures and identify objects and phenomena

  • Asset data is rendered into interactive displays, visualisations, and alerts that highlight the risk’s location and urgency

  • Directs expert staff only to where they’re needed

  • Cloud-based SaaS platform that can be integrated with software applications, and accessed via browser, tablet, or smartphone

Deployed by

In more detail

Full description

When visibility across land, assets, and installations is limited, industrial risks such land movement, tailings drift, or chemical contamination can go unnoticed and quickly become hazardous.

Through a combination of artificial intelligence and satellite data feeds, this SaaS solution provides total visibility across all assets and turns data into powerful visual insights that alert you to developing risks. This allows you to respond in a timely manner, prevent costs from escalating, and minimise environmental impact.

The challenge

Lack of advance warning for large scale issues like land and tailings movement, pipeline leaks, and chemical seeps in a mining operation can mean huge financial and safety impact, from loss of productivity, hefty remediation costs, and reputational damage.

To avoid these issues, it’s crucial to identify and respond to events before they have a chance to escalate. But this can be difficult with traditional methods like manual inspections of assets, which are expensive, time consuming, and infrequent. Catching an event in its early stage requires real-time, continuous monitoring of asset data, and a way to transform this information into actionable insights.

The approach

This satellite data analytics solution collects data from assets using a variety of satellite sources, aircraft, drones, and fixed or persistent sensor platforms. Through AI algorithms, the spectral signatures contained in this data are isolated and used to identify objects and phenomena.

Detection algorithms are then used to create interactive displays and visualisations that show exactly what’s taking place. Through this powerful technology, operators are alerted to developing risks, and are provided with their exact locations and severity measures so that they can respond quickly and appropriately. This SaaS platform has no installation requirements and accurately monitors change and alerts based on business rules you set up.

This flexible platform can work across numerous use cases for mining including land management around mines, tracking acid mine damage, tailings monitoring, methane tracking for coal mines, pipeline spill tracking and metal concentration in the local environment.

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Tech specification

Data acquisition
Satellites, aircraft, drones, fixed assets
Technical scope
Physical, chemical and biological analysis
Cloud usage
AWS by default
Imagery requirements
Pixel depth >=8bit, resolution from 30m to 0.3m
Mobile platforms
iOS and Android
Token secured, REST-based, JSON output
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