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Testing few-shot land cover classification on Sentinel-2 satellite imagery
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The original paper tested on mini-ImageNet, a benchmark of 100 everyday object categories (dogs, cars, furniture) photographed with regular cameras. We replace this with Sentinel-2 satellite imagery — a fundamentally different visual domain with overhead perspective, multispectral sensors, and land cover semantics instead of object recognition. We also use a within-domain split (common vs. rare land cover from the same dataset) rather than the original same-domain setup, and we use only the RGB bands (3 out of 13 available Sentinel-2 bands).
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We use the EuroSAT dataset containing 27,000 real Sentinel-2 satellite image patches covering 10 land cover types across Europe. We split these into 7 common types (forest, cropland, urban, etc.) used for training, and 3 types (herbaceous vegetation, permanent crop, river) held back as "rare" classes. The model learns from the common types, then must classify the rare types using only 1, 5, or 20 labeled examples. We evaluate over 600 random classification tasks and report mean accuracy with 95% confidence intervals.
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We test whether Prototypical Networks — an AI method designed to learn new categories from very few examples — works for classifying land cover types in real satellite imagery. The original method was only tested on everyday photographs (animals, objects, vehicles). We apply it to Sentinel-2 Earth observation data.
https://w3id.org/sciencelive/np/RALGj6JatoumnOiiTUie18OUEVGpeAKuhvUkLpAa2Wjjc/few-shot-sentinel-2
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Anne Fouilloux
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2026-04-17T12:43:55.738Z
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