GlobalX integrates advanced satellite remote sensing with proprietary analytics to support safe, transparent, and data-driven decision-making across the nuclear lifecycle. Our monitoring tools provide critical insight for operators, regulators, and stakeholders involved in nuclear power generation, uranium mining, and long-term waste management.
Optical Multispectral
- Monitoring surface water near nuclear plants and uranium mine tailings
- Vegetation health assessment to detect environmental stress around facilities and containment areas
- Mapping land use changes near exclusion zones, mining leases, or legacy disposal sites
Synthetic Aperture Radar (SAR)
- Detecting ground movement near deep geological repositories, dry cask storage, and tailings dams
- Monitoring embankment integrity at uranium mill sites and decommissioned nuclear plants
- Supporting early detection of structural instability
Thermal Infrared
- Identifying abnormal heat patterns from reactor cooling systems or buried waste storage
- Monitoring subsurface thermal anomalies linked to leachate, process ponds
- Verifying heat discharge compliance into water bodies from nuclear or processing facilities
Hyperspectral Imaging
- Mapping radionuclide-associated spectral signatures in uranium mine runoff or legacy contamination zones
- Identifying mineralogical changes related to uranium extraction, leaching, or weathering
- Supporting baseline geochemical characterization for new reactor sites or mining projects |
AI/ML Model Examples
Supervised Learning
- Classifying land cover changes around nuclear plants and ISR mining zones
- Detecting known contamination patterns near storage or tailings sites
- Verifying remediation performance via time-stamped site imagery
Deep Learning
- Segmenting satellite or drone imagery to identify waste handling zones, cooling infrastructure, or disturbed mining footprints
- Mapping anomalies on surface and subsurfce zones
Time-Series Forecasting
- Forecasting emissions trends or discharge risks from nuclear or mining operations
- Predicting seasonal water use patterns
- Anticipating infrastructure degradation under climate stressors
Unsupervised Learning & Anomaly Detection
- Identifying unexpected activity
- Isolating outlier behaviors in temperature, radiation, or vegetation indices
- Flagging changes inconsistent with expected remediation or mining operations
Multi-Modal/Hybrid Models
- Integrating satellite, sensor, and process data
- Supporting regulatory compliance monitoring and predictive maintenance
- Enabling dynamic prioritization of inspection and ground-truthing efforts