Overview
Clarity is a cloud platform for organizing, exploring, and analyzing hyperspectral imagery and for training models that apply those analyses to new data.
Choose where to start
| Goal | Guide |
|---|---|
| Import, organize, or process data | Files |
| Find managed imagery across folders and projects | Managed Imagery Catalog — Alpha |
| Inspect imagery, create labels, or run analyses | Spectral Explorer |
| Curate reusable spectral signatures | Spectral Libraries |
| Prepare versioned training and evaluation data | Datasets |
| Train, compare, and apply machine-learning models | Models |
| Ask for contextual analysis or research help | Clarity AI |
| Automate supported workflows in Python | SDK |
Use Projects to keep related files, workspaces, datasets, models, and spectral libraries in one working context.
Typical model workflow
- Upload data, confirm its processing level, and open it in Spectral Explorer.
- Inspect the imagery and create finalized labels or reference spectra.
- Create and finalize a dataset version.
- Choose a model type, train it, and inspect held-out metrics and predictions.
- Run inference on compatible imagery and validate the result for the intended application.
For a guided example, follow the plastics unmixing tutorial.
Other resources
- What's new: user-facing release history.
- Clarity AI example prompts and capabilities and tools: current agent guidance.
- ArcGIS Pro add-in: support-assisted integration for existing customers.
- Account access or support@metaspectral.com: access and help.