Active Learning

Active Learning is an iterative machine learning approach where the model actively selects which unlabeled examples would be most valuable to label, rather than training on randomly selected samples. By strategically choosing the most informative or uncertain samples for human annotation, active learning achieves strong model performance with significantly fewer labeled examples than passive random sampling. The process cycles between model training, uncertainty estimation, sample selection, human annotation, and model retraining until a performance target is met or the annotation budget is exhausted. Active Learning for Satellite Image AnnotationGeospatial applications benefit enormously from active learning because labeling satellite imagerySatellite ImagerySatellite imagery consists of photographs and data captured by Earth observation satellites orbiting the planet. Thes... is a specialized and costly task. Instead of randomly selecting thousands of image tiles for expert annotation, active learning identifies the specific tiles where the current model is most uncertain or where labeling would provide the greatest information gain. Uncertainty sampling selects pixels or patches where the classifier is least confident, typically near class boundaries or in ambiguous spectral regions. Query-by-committee methods maintain multiple models and select samples where they disagree most. Diversity-based strategies ensure selected samples represent the geographic and spectral variety of the study area, preventing redundant labeling of similar scenes. Efficiency Gains and Practical ImplementationStudies consistently show that active learning achieves target classification accuracy with 30-70% fewer labeled samples compared to random selection in remote sensingRemote SensingRemote sensing is the science of collecting data about Earth's surface without direct physical contact, primarily usi... applications. This translates directly to reduced annotation costs and faster model deploymentModel DeploymentModel Deployment is the process of integrating a trained machine learning model into a production environment where i.... Modern active learning workflows integrate with GISGISGeographic Information Systems (GIS) enable users to analyze and visualize spatial data to uncover patterns, relation... platforms, presenting selected samples in a map-based interface where analysts can efficiently label features in geographic context. Batch active learning selects groups of informative samples at each iteration, accommodating practical workflows where annotation happens in scheduled sessions rather than continuously.