Adapting Actively on the Fly: Relevance-Guided Online Meta-Learning with Latent Concepts for Geospatial Discovery

Researchers propose a geospatial discovery framework combining active learning, online meta-learning, and concept-guided reasoning to efficiently identify contamination hotspots like PFAS under limited sampling budgets. The approach uses concept relevance to guide uncertainty sampling and improve generalization in dynamic environmental monitoring scenarios.

This research addresses a critical gap in environmental monitoring where traditional machine learning methods fail due to sparse, biased geospatial labels and extremely limited ground truth data. PFAS contamination detection exemplifies a broader class of problems in disaster response and public health where sampling is expensive and domain expertise is scarce. The proposed framework's novelty lies in combining three complementary approaches: active learning reduces data collection costs by intelligently selecting which locations to sample, online meta-learning enables the model to adapt rapidly as environmental conditions change, and concept-guided reasoning leverages domain knowledge such as land cover patterns and proximity to known pollution sources.

The relevance-weighted uncertainty sampling strategy represents the technical contribution with practical implications. Rather than treating uncertainty uniformly, the framework modulates it by learned relevance scores derived from available geospatial concepts, effectively prioritizing uncertain regions where domain factors suggest higher contamination likelihood. This semantic-aware approach should outperform purely uncertainty-driven methods in sparse data regimes. The meta-batch formation strategy further enhances performance by promoting diversity during online updates, preventing the model from overfitting to early biased observations.

For environmental organizations and public health agencies, this framework could substantially reduce monitoring costs while improving detection accuracy in under-resourced regions. The methodology generalizes beyond PFAS to any sparse-label geospatial discovery task, making it relevant across climate science, epidemiology, and disaster response. Adoption would require integration with existing geospatial data pipelines and stakeholder buy-in regarding algorithmic recommendations for sampling prioritization.

• →Framework combines active learning, online meta-learning, and concept-guided reasoning for efficient geospatial discovery under budget constraints.
• →Concept-weighted uncertainty sampling modulates data collection strategy using domain knowledge like land cover and source proximity.
• →Approach addresses sparse, biased labels endemic to environmental monitoring and public health applications.
• →Demonstrated on PFAS contamination detection but generalizes to other geospatial discovery problems.
• →Online meta-learning enables dynamic adaptation as environmental conditions change over time.