Find answers in
Earth observation data.
Explore minerals, vegetation, and emissions with our remote-sensing team. We bring the imagery and analysis together in Clarity so you can understand the findings and look deeper.
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Explore the applications
Explore how Clarity AI supports specialized spectral Earth observation applications.
Mining & Minerals
Map mineral and alteration signatures to focus your next field visit.
Agriculture & Yield
Detect crop stress and biochemical changes before visible damage occurs.
Defense & Intelligence
Detect concealed materials and sub‑pixel anomalies using spectral intelligence.
Wildfire Intelligence
Map burned ground and changing vegetation to guide field assessment and recovery.
Emissions Monitoring
Locate methane emissions from satellite imagery and inspect the evidence behind each detection.
The expertise behind every Clarity AI workflow.
Match the data to the question
Clarity AI helps teams evaluate whether a sensor's spectral, spatial, and temporal characteristics fit the target. We work across satellite, airborne, drone, and customer-provided data, including PRISMA, EnMAP, EMIT, and Tanager imagery.
Use the right analysis for the signal
Combine calibration, corrections, spectral indices, classical target detection, patented spectral unmixing, and deep learning according to the scientific problem—not a generic AI recipe.
Bring your scientists or ours
Metaspectral can help scope, design, and validate the workflow, or your science team can define the methods and approve the results. Clarity AI preserves that expertise for the next scene, region, or campaign.
From spectral EO feasibility to repeatable analysis.
Clarity AI brings data selection, preparation, analysis, validation, and campaign orchestration into one workflow while keeping the scientific decisions visible.
Determine whether spectral EO fits the problem
Define the target, geography, timing, decision threshold, and required evidence. Clarity AI helps assess whether the observable signal and available sensors can support the intended application before the team commits to a larger program.
Identify suitable imagery and providers
Evaluate open and commercial sources by spectral range, spatial resolution, coverage, revisit frequency, archive availability, tasking options, and acquisition conditions. Keep the selected scenes and the rationale for using them connected to the project.
Prepare the data for analysis
Apply the required calibration, atmospheric and geometric corrections, masking, co-registration, normalization, and resampling. Add reference spectra, ground truth, and contextual layers without losing track of how the analysis-ready data was produced.
Build and validate the spectral analysis
Run classical spectral methods, patented spectral unmixing, and deep-learning models individually or together. Compare results with labels, ground truth, reference data, or expert review to determine whether the application is useful and where its limits are.
Extend a successful method into a campaign
Clarity AI agents can orchestrate the approved workflow across new scenes, regions, and time periods using our compute or yours. Data lineage, method versions, assumptions, review decisions, and outputs stay connected as the campaign grows.
Target and anomaly maps, mineral or material class layers, change products, plume packages, confidence context, spectral evidence, and an analyst-ready report matched to the objective.
See the methods, results, and limitations for yourself.
Our technical articles document the datasets, sensors, methods, comparisons, and claim boundaries behind selected Earth-observation work.
A Tetracorder Emulator Reached 0.91 F1 and Transferred from EMIT to Tanager
A benchmark and transfer demonstration showing how expert mineralogical logic can be packaged for analysis across sensors.
Read the analysis
Detecting and Quantifying Methane Plumes with PRISMA and EnMAP
A reproducible workflow that separates plume detection from source-rate estimation and compares results with published values.
Read the analysis
A Workflow Is More Than a Prompt: Preserving Expert Judgment in Hyperspectral Analysis
How teams can encode methods, review logic, sensor context, and scientific guardrails in a repeatable Clarity AI workflow.
Read the analysis