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[2602.17605] Adapting Actively on the Fly: Relevance-Guided Online Meta-Learning with Latent Concepts for Geospatial Discovery
Summary
This paper presents a novel framework for geospatial discovery that integrates active learning and online meta-learning, focusing on relevance-guided strategies to enhance target identification in dynamic environments with limited data.
Why It Matters
The research addresses critical challenges in fields like environmental monitoring and public health, where efficient data collection is essential. By improving the ability to uncover hidden targets under resource constraints, this work has implications for disaster response and health safety, making it relevant for practitioners and researchers alike.
Key Takeaways
- Introduces a unified framework combining active learning and meta-learning for geospatial discovery.
- Proposes a concept-weighted uncertainty sampling strategy to enhance target detection.
- Demonstrates effectiveness through experiments on real-world datasets, specifically PFAS contamination.
- Focuses on semantic diversity during online updates to improve model generalization.
- Addresses the limitations of existing methods in sparse and biased geospatial data environments.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.17605 (cs) [Submitted on 19 Feb 2026] Title:Adapting Actively on the Fly: Relevance-Guided Online Meta-Learning with Latent Concepts for Geospatial Discovery Authors:Jowaria Khan, Anindya Sarkar, Yevgeniy Vorobeychik, Elizabeth Bondi-Kelly View a PDF of the paper titled Adapting Actively on the Fly: Relevance-Guided Online Meta-Learning with Latent Concepts for Geospatial Discovery, by Jowaria Khan and 3 other authors View PDF HTML (experimental) 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 *relev...