Few-Shot Learning

Few-Shot Learning is a machine learning paradigm designed to enable models to generalize to new classes or tasks with only a very small number of labeled examples, typically between one and ten per category. This contrasts with conventional deep learning, which often requires thousands or millions of labeled examples per class. Few-shot methods achieve this through meta-learning (learning to learn), metric-based approaches that compare new examples to prototypes, or adaptation strategies that fine-tune pretrained models with minimal data. Addressing Data Scarcity in Geospatial AIFew-shot learning is critically important for geospatial applications where labeled training data is a persistent bottleneck. Annotating satellite imagerySatellite ImagerySatellite imagery consists of photographs and data captured by Earth observation satellites orbiting the planet. Thes... requires domain expertise in remote sensingRemote SensingRemote sensing is the science of collecting data about Earth's surface without direct physical contact, primarily usi... and geography, making it far more expensive than labeling everyday photographs. Rare land cover types, newly emerging features like construction sites, or geographically unique landscapes may have very few available examples. Few-shot land cover classificationLand Cover ClassificationLand cover classification is the process of categorizing Earth's surface into distinct classes such as forest, cropla... enables rapid deployment in new regions by requiring only a few labeled pixels per class instead of dense training maps. Disaster response benefits from few-shot object detectionObject DetectionObject Detection is a computer vision technique that identifies and localizes specific objects within images or video... that can quickly learn to identify damaged structures from just a handful of post-event examples. Approaches and Integration with Foundation ModelsMetric-based few-shot methods like Prototypical Networks learn to compare new examples against class prototypes in an embedding space. Model-Agnostic Meta-Learning (MAML) trains models that can be quickly adapted to new tasks with gradient updates from few examples. Foundation models pretrained with self-supervised learningSelf-Supervised LearningSelf-Supervised Learning is a machine learning paradigm where models learn representations from unlabeled data by sol... on large satellite image archives provide strong starting representations that dramatically improve few-shot performance. The combination of foundation model pretraining and few-shot fine-tuning is emerging as a practical workflow for deploying geospatial AI in data-scarce regions.