BETTER DATA. STRONGER FOUNDATIONS.

Prepare with precision.
Train with confidence.

Your land-cover dataset deserves a careful start. Audit image manifests, understand class balance, and uncover duplicates before your next experiment.

Manifest validationSplit leakage checksSHA-256 matching
Conceptual satellite-style landscape of forest, agricultural fields, a river and built-up areas, transitioning to land-cover classes and image tilesREMOTE SENSING / LAND-COVER DATASETSAI-GENERATED CONCEPT · NOT ANALYSIS OUTPUT
MANIFEST RECORDS▦—

Rows in the current audit

CLEAN RECORDS✓—

First valid entry per path

REPEATED PATHS⊞—

Additional path occurrences

SPLIT LEAKAGE↗—

Paths found across splits

Your preparation workspace

01 / Import   →   02 / Inspect   →   03 / Export

01Manifest audit

CSV / TXT

Required columns: image, label, split. Audit data stays in this browser tab.

One image per row. Use train, val, validation or test as split values.

Run an audit to see validation results and flagged records.