Adapting Actively on the Fly: Relevance-Guided Online Meta-Learning with Latent Concepts for Geospatial Discovery
2026-02-19 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial IntelligenceComputers and SocietyMachine Learning
AI summaryⓘ
The authors present a new method to help find hidden environmental hazards, like contaminants, more efficiently when data is scarce and conditions change. They combine techniques that actively choose where to sample next, quickly adapt from new data, and use domain knowledge about important factors affecting pollution presence. Their method considers how relevant different concepts are, such as nearby land types, to improve where and what to sample. Testing on real contamination data shows their approach can reliably uncover targets with limited and changing information.
active learningonline meta-learninguncertainty samplingconcept relevancegeospatial dataPFAS contaminationenvironmental monitoringreinforcement learning
Authors
Jowaria Khan, Anindya Sarkar, Yevgeniy Vorobeychik, Elizabeth Bondi-Kelly
Abstract
In many real-world settings, such as environmental monitoring, disaster response, or public health, with costly and difficult data collection and dynamic environments, strategically sampling from unobserved regions is essential for efficiently uncovering hidden targets under tight resource constraints. Yet, sparse and biased geospatial ground truth limits the applicability of existing learning-based methods, such as reinforcement learning. To address this, we propose a unified geospatial discovery framework that integrates active learning, online meta-learning, and concept-guided reasoning. Our approach introduces two key innovations built on a shared notion of *concept relevance*, which captures how domain-specific factors influence target presence: a *concept-weighted uncertainty sampling strategy*, where uncertainty is modulated by learned relevance based on readily-available domain-specific concepts (e.g., land cover, source proximity); and a *relevance-aware meta-batch formation strategy* that promotes semantic diversity during online-meta updates, improving generalization in dynamic environments. Our experiments include testing on a real-world dataset of cancer-causing PFAS (Per- and polyfluoroalkyl substances) contamination, showcasing our method's reliability at uncovering targets with limited data and a varying environment.