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Tested on 10 broad EuroSAT land cover categories at 10 m ground resolution using RGB bands only. https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8/few-shot-eurosat-synthesis https://w3id.org/sciencelive/o/terms/hasLimitationsDescription Not tested on fine-grained habitat discrimination such as distinguishing vegetation subtypes within Natura 2000 sites. Only RGB bands used — near-infrared and shortwave infrared bands, which are critical for vegetation analysis, were not included. Results may differ for non-optical sensors (SAR, LiDAR) or very high resolution imagery. The within-domain experiment used a different base/novel class split than a real monitoring scenario would require. https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8/few-shot-eurosat-synthesis https://w3id.org/sciencelive/o/terms/hasRecommendationDescription For Earth observation researchers with limited labeled satellite data: if any labeled satellite imagery is available for your region (even for different classes than your target), use within-domain few-shot learning — train on common classes and classify rare ones. If no satellite training data exists at all, use an off-the-shelf ImageNet-pretrained model as a feature extractor for initial screening, then invest in labeling a small satellite dataset to improve accuracy. Complex meta-learning pipelines are not necessary — standard supervised pretraining achieves comparable results. https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8/few-shot-eurosat-synthesis https://w3id.org/sciencelive/o/terms/hasSynthesisDescription Four experiments comparing within-domain and cross-domain few-shot learning on Sentinel-2 satellite imagery show that within-domain transfer (training on common satellite land cover classes, classifying rare ones) achieves 82% accuracy with 5 labeled examples per class, while cross-domain transfer from everyday photographs achieves 67–76% depending on backbone architecture and training method. The domain gap between photographs and satellite imagery reduces accuracy by 6–15 percentage points. Supervised pretraining on everyday photographs matches episodic meta-learning with 12 times less training time, but both cross-domain approaches remain below within-domain accuracy. https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8/provenance https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8/assertion http://www.w3.org/ns/prov#wasAttributedTo https://orcid.org/0000-0002-1784-2920 https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8/pubinfo http://www.wikidata.org/entity/Q110797734 https://w3id.org/np/o/ntemplate/hasLabelFromApi few-shot learning - machine learning approach that enables a system to learn new tasks or recognize new objects from only a few examples or demonstrations, rather than requiring extensive data http://www.wikidata.org/entity/Q199687 https://w3id.org/np/o/ntemplate/hasLabelFromApi remote sensing - acquisition of information about an object or phenomenon without making physical contact with the object, especially the Earth http://www.wikidata.org/entity/Q3001793 https://w3id.org/np/o/ntemplate/hasLabelFromApi land cover - nature of the physical material at the surface of the earth http://www.wikidata.org/entity/Q6027324 https://w3id.org/np/o/ntemplate/hasLabelFromApi transfer learning - research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem https://orcid.org/0000-0002-1784-2920 http://xmlns.com/foaf/0.1/name Anne Fouilloux https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8 http://purl.org/dc/terms/created 2026-04-18T18:55:18.984Z https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8 http://purl.org/dc/terms/creator https://orcid.org/0000-0002-1784-2920 https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8 http://purl.org/dc/terms/license https://creativecommons.org/licenses/by/4.0/ https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8 http://purl.org/nanopub/x/introduces https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8/few-shot-eurosat-synthesis https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8 http://purl.org/nanopub/x/wasCreatedAt https://nanodash.knowledgepixels.com/ https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8 https://w3id.org/np/o/ntemplate/wasCreatedFromProvenanceTemplate https://w3id.org/np/RA7lSq6MuK_TIC6JMSHvLtee3lpLoZDOqLJCLXevnrPoU https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8 https://w3id.org/np/o/ntemplate/wasCreatedFromPubinfoTemplate https://w3id.org/np/RACJ58Gvyn91LqCKIO9zu1eijDQIeEff28iyDrJgjSJF8 https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8 https://w3id.org/np/o/ntemplate/wasCreatedFromPubinfoTemplate https://w3id.org/np/RAukAcWHRDlkqxk7H2XNSegc1WnHI569INvNr-xdptDGI https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8 https://w3id.org/np/o/ntemplate/wasCreatedFromTemplate https://w3id.org/np/RApmrqOEr4f5bJC2vayrTnzhwnuEfAU_I4Pdg8K5JxeBw https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8/sig http://purl.org/nanopub/x/hasAlgorithm RSA https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8/sig http://purl.org/nanopub/x/hasPublicKey MIGfMA0GCSqGSIb3DQEBAQUAA4GNADCBiQKBgQDWv2pJnmDsBOq8OlT1aSvYXSuWT34WOp4FYqEzdnn2F0kqzcFevBqWGZDxJWC0lqCrDEuNfp2QFyPe/+nES9dlHGYIhqPi68fwK6ZiNUotRFxXou+rjFznVvUxtCL8Ede79EBHwWN61QtwSIcU12bLoZsNPFlqQASQ93BJuKlihwIDAQAB https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8/sig http://purl.org/nanopub/x/hasSignature FTGbwGUbqi+nCLOqOznThPR9MOLOcTesabQLyyR5desYZTqLy/St4qAbY50Y781/RcoQGvufwkJEp73g0bXf1UKhSykRwfC/NrC01/kh5emK/uuwxgbVd4+avSqwx3cXSa0i5Esx2Xv3J4WFDm8qXw2GhtmWcdDXkthZHQ6MiW4= https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8/sig http://purl.org/nanopub/x/hasSignatureTarget https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8 https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8/sig http://purl.org/nanopub/x/signedBy https://orcid.org/0000-0002-1784-2920