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47 lines
1.4 KiB
Markdown
47 lines
1.4 KiB
Markdown
# Data Lifecycle
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## Original dataset lifecycle
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1. User or system registers dataset.
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2. Original file/reference is preserved.
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3. Dataset enters `UPLOADED` or `CREATED`.
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4. Validation extracts basic file and geospatial metadata.
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5. Metadata is persisted.
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6. Dataset becomes `READY`, `REQUIRES_CRS` or failure state.
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## Derived dataset lifecycle
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1. Operation is requested from a ready parent dataset.
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2. AnalysisRun is created.
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3. Job produces derived artifact.
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4. Derived dataset references parent dataset and analysis run.
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5. Derived metadata is extracted.
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6. Derived layer can be rendered/exported.
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## Detection lifecycle
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1. Detection run is created.
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2. Inputs and parameters are stored.
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3. Model/import produces candidate features.
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4. Features are georeferenced.
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5. Features are validated.
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6. Features are persisted as detections.
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7. Optional QA/QC compares them to reference features.
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8. Detections may be exported.
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## Segmentation lifecycle
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1. Segmentation run is created.
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2. Raster and model parameters are stored.
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3. Model/import produces masks.
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4. Masks are georeferenced.
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5. Optional polygonization creates vector features.
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6. Features and/or mask paths are persisted.
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7. QA/QC and export may run.
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## Deletion and archival
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- Deleting a project should not immediately physically delete files unless cleanup is requested.
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- Archival removes items from active lists but keeps reproducibility.
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- Physical cleanup must never leave dangling database references.
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