Most people think overviews are thumbnails. They’re actually a critical performance mechanism. Without them, displaying a 10GB satellite image on a 1080p screen requires reading and downsampling all 10GB in real time. That’s backward.
Overviews are pre-computed resolution pyramids. Store the original image plus versions at 1/2, 1/4, 1/8, and 1/16 resolution inside the file. When a user zooms out, the reader selects the matching overview level and fetches only those pixels. Zoom to see a city on a continent-scale map? You read 1% of the pixels, not 100%.
The math: a pyramid adds roughly 33% to file size (1/4 + 1/16 + 1/64… converges to 1/3). You sacrifice disk space for exponential latency gains. Always worth it.
The danger: resampling algorithm matters. For categorical data (Land Cover classifications), use Nearest Neighbor. It preserves exact values. Average or Cubic resampling on categorical data invents values that don’t exist—you get a fake class between two real ones. For continuous data (satellite imagery, DEMs), Cubic or Average smooths pixels naturally.
Store overviews internally, not as external .ovr files. Cloud workflows need everything in one request. External files break that atomicity.
Generate overviews to powers of 2 until the smallest fits in a single tile (typically 256×256).
The rule: Storage is cheap; latency is expensive. Spend the 33% disk space. Always build internal overviews for rasters on cloud storage.
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