STAC Items: Discovering Satellite Assets with Explicit Metadata
A STAC Item describes a spatiotemporal asset using a GeoJSON-based structure and links to data.
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A STAC Item describes a spatiotemporal asset using a GeoJSON-based structure and links to data.
A Cloud Optimized GeoTIFF organizes imagery so compatible clients can request useful byte ranges and overview levels.
GeoTIFF combines raster values with georeferencing and related metadata.
Model drift describes changing input or performance conditions after deployment.
Counting classified pixels gives map area under the classifier’s decisions.
A change model compares observations or derived features across time.
Feature importance describes how a fitted model uses predictors under a particular calculation.
Regression estimates a continuous target from imagery and other predictors.
Object detection predicts locations and classes of defined objects.
Semantic segmentation assigns classes across image pixels using spatial context.
A model score, vote fraction and calibrated probability are different quantities.
Class imbalance occurs when some classes occupy far fewer samples or area than others.
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