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.

翻译：在许多现实场景中，如环境监测、灾害响应或公共卫生领域，数据采集成本高昂、环境动态变化，在资源严格受限的条件下，从未观测区域进行策略性采样对于高效发现隐藏目标至关重要。然而，稀疏且存在偏差的地理空间真实数据限制了现有基于学习的方法（如强化学习）的适用性。为此，我们提出了一个统一的地理空间发现框架，该框架融合了主动学习、在线元学习与概念引导推理。我们的方法基于共享的*概念相关性*概念构建了两项关键创新——该相关性刻画了领域特定因素如何影响目标存在性：一是*概念加权不确定性采样策略*，其通过基于易获取的领域特定概念（如土地覆盖、污染源邻近度）学习得到的相关性来调节不确定性；二是*相关性感知的元批次构建策略*，该策略在在线元更新过程中促进语义多样性，从而提升动态环境下的泛化能力。我们的实验包括在真实世界致癌性PFAS（全氟及多氟烷基化合物）污染数据集上的测试，结果表明本方法在数据有限且环境变化的条件下具有可靠的目标发现能力。