About
A mining client, operating an extensive network of mine closure sites across Australia and internationally, sought to improve the efficiency, accuracy and scalability of invasive weed detection across its landholdings. With multiple sites and hundreds of weed species requiring ongoing monitoring, the company aimed to establish a costeffective, repeatable method for identifying problem vegetation. Beginning with a pilot site in Queensland, the initial focus was on detecting a highly invasive weed posing environmental, firerisk and rehabilitation challenges. Geospatial Intelligence was engaged to develop a remote sensing–based assessment of site conditions, identify key risks and build a scalable framework for future weedmanagement operations.
The Challenge
The mining client and its global closure sites face several operational and environmental pressures that threaten rehabilitation outcomes, landstability goals and longterm ecosystem recovery.
The primary risks identified included:
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IMAGE RESOLUTION
Existing imagery did not provide the spatial detail required to reliably detect the weed species. Highresolution, 16bit, and appropriately timed acquisitions were essential to ensure accurate specieslevel classification.
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CLIMATE & SITE VARIABILITY
Changing climate, variable soil types, differing landforms and inconsistent site histories influence how weed species take hold across mine sites. This variability required a flexible, sitespecific sensor strategy rather than a onesizefitsall solution.
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MULTISPECIES & MULTISITE COMPLEXITY
With the potential to expand weed detection across 80+ sites and hundreds of species, the client needed a method capable of scaling globally while maintaining accuracy and cost-efficiency.
Through this research initiative, the mining client gained access to a stateoftheart remote sensing workflow, enabling accurate species identification, scalable monitoring and datadriven decisionmaking across a diverse rehabilitation portfolio. Our findings will serve as the foundation for continued innovation in minesite rehabilitation, ensuring longterm environmental performance and operational sustainability for the mining client and its global operations.
Outcome
We successfully identified individual invasive weed plants by combining advanced machine learning techniques with extremely highresolution imagery. Following detection, we provided evidencebased recommendations on the optimal frequency of future data capture and analysis to effectively monitor changes in weed distribution over time.