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

2026-02-19

AI Summary

This work addresses the challenges of geospatial target discovery in dynamic scenarios such as environmental monitoring and disaster response, where data acquisition is costly, annotations are sparse, and label bias is prevalent. To tackle these issues, the authors propose a novel framework that integrates active learning, online meta-learning, and concept-guided reasoning. By leveraging domain concepts—such as land cover—to model semantic relationships, the framework introduces a concept-weighted uncertainty sampling strategy and a relevance-aware meta-batch construction mechanism to dynamically optimize sample selection and model updates. Evaluated on a real-world PFAS contamination dataset, the approach demonstrates significant improvements in both target discovery performance and generalization under resource constraints and shifting environmental conditions.

Technology Category

• Machine Learning: Active Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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.

Problem

• geospatial discovery
• active learning
• online meta-learning
• concept relevance
• sparse ground truth

Innovation

• concept relevance
• online meta-learning
• active learning
• geospatial discovery
• uncertainty sampling