Jain P., Marcos D., Ienco D., Interdonato R., Dhakal A., Jacobs N., Berchoux T. (2024). Aligning geo-tagged clip representations and satellite imagery for few-shot land use classification. p. 1-5. International Symposium on Geoscience and Remote Sensing: IGARSS 2024, 2024/07/07-12, Athens (Greece). Granular project (Grant agreement ID: 101061068).
https://doi.org/10.5281/zenodo.14196720
https://doi.org/10.5281/zenodo.14196720
Titre : | Aligning geo-tagged clip representations and satellite imagery for few-shot land use classification |
Auteurs : | P. Jain ; D. Marcos ; D. Ienco ; R. Interdonato ; A. Dhakal ; N. Jacobs ; T. Berchoux |
Type de document : | Actes de Congrès ou Communication isolée |
Année de publication : | 2024 |
Format : | p. 1-5 |
Note générale : | Granular project (Grant agreement ID: 101061068) |
Langues : | Anglais |
Langues du résumé : | Anglais |
Catégories : |
Catégories principales 04 - DEVELOPPEMENT LOCAL ET REGIONAL ; 4.1 - Territoire (généralités). Economie Régionale et Spatiale. Aménagement du TerritoireThésaurus IAMM TECHNIQUE D'IMAGERIE ; APPRENTISSAGE ; CLASSIFICATION ; TELEDETECTION ; UTILISATION DES TERRES ; METHODE ; MODELE ; PHOTOINTERPRETATION |
Résumé : | A major difference between ground-level and satellite imagery of landscapes lies in their semantic granularity: ground-level images tend to offer details on objects and human activities, while satellite images provide broader geographic context but, typically, with coarser semantics. This study aims to leverage this complementary information by integrating fine-grained insights from a ground-level view into the analysis of satellite image data. To achieve this integration, we propose to align a satellite image representation with co-located geo-tagged ground-level image CLIP representations. This method focuses on enriching satellite image visual features by leveraging the inherent visual characteristics found in ground-level images as a reference in a contrastive manner, without relying on additional textual information to guide the learning process. We evaluate the quality of the learned representations on the EuroSAT benchmark in various few-shot settings. |
Note de contenu : | Preprint |
Congrès : | International Symposium on Geoscience and Remote Sensing: IGARSS 2024, 2024/07/07-12, Athens (Greece) |
Cote : | Online |
URL / DOI : | https://doi.org/10.5281/zenodo.14196720 |
Documents numériques (1)
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