Identify
Determine whether a material, signature, condition, or target is present in the available spectral evidence.
What Is Material Intelligence?
The ability to identify, classify, monitor, and explain materials from spectral evidence—and turn the result into an output that fits a real workflow.
Material-level decisions
More than a data product
Hyperspectral data can reveal narrow spectral structure related to composition and condition. Material Intelligence connects that signal with data quality, analytical models, confidence context, validation, and delivery so the evidence can be used responsibly. The result may be a sorting decision, material class layer, anomaly map, plume overlay, or monitoring report.
Classify
Assign materials or conditions to meaningful classes for sorting, mapping, monitoring, or review.
Monitor
Track material-related signals and changes across time, geography, or an operating process.
Explain
Surface the evidence, confidence, caveats, provenance, and validation path behind a result.
Operationalize
Deliver the result as a sort decision, map layer, alert, report, API output, or review package.
A disciplined workflow
Six steps to operationalize your results
Clarity gives teams a practical path from raw spectral data to a usable deliverable. Each workflow connects data preparation, analysis, review, validation, and export so the result can move into the tools and decisions that follow.
- 01
Bring in the data
Bring industrial sensor data, satellite or airborne imagery, reference spectra, and customer datasets into Clarity.
- 02
Prepare and quality-check
Use Clarity workflows to apply calibration, masking, normalization, metadata checks, and other quality controls before analysis.
- 03
Run the analysis
Apply the method suited to the question, including material identification, classification, unmixing, target or anomaly detection, change detection, or supported quantification.
- 04
Review context and confidence
Review the result with its geospatial, operational, temporal, or domain context, including where the evidence is strong, weak, or ambiguous.
- 05
Validate against evidence
Compare the output with fit-for-purpose evidence such as samples, labels, field observations, measurements, or expert review.
- 06
Deliver it to the workflow
Export map and confidence layers for ArcGIS or another GIS, send classifications to sorting or control systems through an API, or generate a reviewable report for technical and operational teams.
Where it creates value
When composition changes the decision
Industrial sorting and recovery
Distinguish difficult polymers, mixed materials, multilayer packaging, mineral streams, and other visually similar inputs.
Mining and minerals
Map material signatures, alteration patterns, and candidate targets to prioritize field review, sampling, or exploration.
Agriculture and environment
Surface crop, soil, water, or surface-condition indicators that guide scouting, sampling, monitoring, and expert review.
Emissions monitoring
Review plume-related spectral signals, track change, and produce outputs matched to the sensor and monitoring program.
Mission and security workflows
Prioritize material or spectral anomalies where the target is distinguishable and the use case is appropriately governed and validated.
Trust is part of the workflow
Make the result reviewable
Material-level decisions can affect recovery economics, field campaigns, environmental response, regulatory reporting, and mission priorities. Teams need to see how a result was produced and where its limits are.
- Data source, metadata, and preprocessing history
- Quality checks and known limitations
- Spectral evidence supporting the result
- Confidence, caveats, and review conditions
- Model, method, and spectral-library provenance
- Validation completed and validation still required
Start with one material or decision
Define what you need to identify or monitor, which decision the result should improve, what evidence will make it trustworthy, and how success will be validated.