. . . . . . "Cross-domain few-shot Sentinel-2 classification with simple Prototypical Networks" . . . . . . "Guo et al. used a deeper ResNet-10 backbone (4.9 million parameters) with 224×224 pixel images and 40,000 training episodes. We used a simpler 4-block CNN with 84×84 images and 5,000 episodes to test whether the cross-domain transfer works even with a minimal architecture." . . "We trained a Prototypical Network with a simple 4-block convolutional backbone (110,000 parameters) on 38,400 mini-ImageNet photographs of everyday objects using 5,000 episodic training steps. We then evaluated the trained model on EuroSAT, a dataset of 27,000 real Sentinel-2 satellite image patches covering 10 land cover types across Europe. Classification was evaluated over 600 random 5-way tasks with 5, 20, and 50 labeled examples per class." . "Testing whether Prototypical Networks — an AI method that classifies new categories from a few labeled examples by computing distances to class prototypes — can transfer from everyday photographs to Sentinel-2 satellite imagery for land cover classification." . . . "Anne Fouilloux" . "2026-04-18T15:53:29.280Z"^^ . . . . . "NP created using Declaring a replication study design according to FORRT" . . "RSA" . "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" . "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" . . .