Toulouse Hyperspectral Dataset evaluation
The Toulouse Hyperspectral Data Set is a public land-cover dataset with standard train and test splits. It can be used to evaluate a Clarity Classification model against a reproducible external protocol.
Use this guide to define a comparable evaluation, record the evidence needed to reproduce it, and interpret the example visualization.
Reproducible evaluation
Before reporting a result, record:
- the official dataset release and split identifier;
- the exact Clarity dataset and model version IDs;
- the source bands, center wavelengths, and processing level;
- preprocessing, augmentation, model initialization, and hyperparameters;
- the Clarity code version recorded on the trained model;
- the evaluation unit and treatment of unlabeled pixels; and
- the metric aggregation method across classes and splits.
Use the official spatial splits without moving test pixels into training or validation. Report overall accuracy together with macro and per-class precision, recall, F1, and support. Aggregate multiple splits consistently and include variation across them.
Example visualization

Color composite (left) and categorical model output (right) for a Toulouse test tile. This qualitative example has no published accuracy or F1 score; verified scores require the run manifest and exported metrics described above.
Inspect output at full resolution for class confusion, boundary effects, spatial leakage, and systematic errors. A visually plausible map does not replace held-out metrics.
Dataset reference
Thoreau, R., Risser, L., Achard, V., Berthelot, B., and Briottet, X. (2024). “Toulouse Hyperspectral Data Set: A benchmark data set to assess semi-supervised spectral representation learning and pixel-wise classification techniques.” ISPRS Journal of Photogrammetry and Remote Sensing, 212, 323–337.
Dataset website: Toulouse Hyperspectral Data Set