https://w3id.org/np/RARv-ABCr9uAsxKeqU4U_I7aX2d5v-JtgMOfZatwmlmM8
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sub:few-shot-eurosat-synthesis a <https://w3id.org/sciencelive/o/terms/Research-Synthesis>;
dct:subject <http://www.wikidata.org/entity/Q110797734>, <http://www.wikidata.org/entity/Q199687>,
<http://www.wikidata.org/entity/Q3001793>, <http://www.wikidata.org/entity/Q6027324>;
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rdfs:label "Within-domain few-shot learning is recommended over cross-domain transfer for satellite imagery classification";
<https://w3id.org/sciencelive/o/terms/hasConditionsDescription> "Applicable to optical satellite imagery (Sentinel-2, Landsat) for land cover and habitat classification tasks where labeled examples are scarce. Tested on 10 broad EuroSAT land cover categories at 10 m ground resolution using RGB bands only.";
<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/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/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." .
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<http://www.wikidata.org/entity/Q110797734> nt: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> nt: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> nt:hasLabelFromApi "land cover - nature of the physical material at the surface of the earth" .
<http://www.wikidata.org/entity/Q6027324> nt: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" .
orcid:0000-0002-1784-2920 foaf:name "Anne Fouilloux" .
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