发表机构
Mississippi State University（密西西比州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对边缘端地理空间大数据实时异常检测的存储处理通信挑战，本文提出结合H3离散全球网格系统与多尺度下钻逻辑的轻量型方法，可降低99.7%计算开销，高效识别空间持续异常信号，提炼海量数据为可行动洞见。
AI 中文摘要
随着环境、应急、气象和农业等关键应用愈发依赖地理空间数据流中的实时异常检测，该类数据的存储、处理与通信面临诸多挑战。传统方法会将大量数据发送至集中式处理节点以提取洞见，但在大数据场景下，数据量与传输速度持续提升使得该方法愈发不可行。本文提出一种面向边缘端的轻量型地理空间数据流异常检测与定位方法，该方法利用H3离散全球网格系统与多尺度下钻逻辑，大幅降低计算开销，相较传统平面扫描方法的评估量减少99.7%；此外，通过过滤低分辨率下由噪声引发的闪烁异常，可高效识别空间持续的异常信号。结果表明，所提框架能有效将海量地理空间数据提炼为可行动的洞见。
英文摘要
As an increasing number of critical applications, including environmental, emergency, meteorological, and agricultural, rely on real-time anomaly detection in geospatial data streams, challenges related to the storage, processing, and communication of this data arise. Traditionally, large volumes of data have been sent to centralized processing locations for insight extraction. Given the big data context of these applications, this approach becomes increasingly infeasible as data volume and velocity continue to increase. This paper proposes a lightweight edge-oriented approach for anomaly detection and localization for geospatial data streams. By leveraging the H3 discrete global grid system and a multi-scale drill-down logic, the proposed approach significantly reduces computational overhead, achieving a 99.7\% reduction in evaluations compared to traditional flat-scan methods. Furthermore, by filtering out noise-induced flickering anomalies at lower resolutions, spatially-persistent anomalous signals can be efficiently identified. The results demonstrate that the proposed framework effectively distills massive geospatial data into actionable insights.
CommentsAccepted at IEEE GLOBECOM 2026
