遥感时空知识图谱驱动的自然资源要素变化图斑智能净化  

Intelligent Purification of Natural Resource Element Change Polygons Driven by Remote Sensing Spatiotemporal Knowledge Graphs

作　　者：李彦胜 钟振宇 孟庆祥[1] 毛之典 党博 王涛 冯苑君 张永军[1,2] LI Yansheng;ZHONG Zhenyu;MENG Qingxiang;MAO Zhidian;DANG Bo;WANG Tao;FENG Yuanjun;ZHANG Yongjun(School of Remote Sensing and Information Engineering,Wuhan University,Wuhan 430079,China;Technology Innovation Center for Collaborative Application of Natural Resources Data in GBA,Ministry of Natural Resources,Guangzhou 510075,China)

机构地区：[1]武汉大学遥感信息工程学院,武汉4300792 [2]自然资源部粤港澳大湾区自然资源数据协同应用工程技术创新中心,广州510075

出　　处：《地球信息科学学报》2025年第2期350-366,共17页Journal of Geo-information Science

基　　金：国家自然科学基金项目(42371321、42030102)。

摘　　要：【目的】随着深度学习技术的发展,遥感影像自然资源要素变化监测能力得到显著提高。基于深度学习的变化检测技术善于挖掘遥感影像的低层次语义信息,但在区分土地利用类型变化与非土地利用类型变化(如农作物轮作、水位自然变化、森林自然退化等)方面存在局限性。为了保证变化检测的高召回率,深度学习变化检测方法往往产生大量虚警变化图斑,仍需大量人工作业工作量来排除虚警变化图斑。【方法】针对这一问题,本文提出了遥感时空知识图谱驱动的自然资源要素变化图斑净化算法。该方法可以在保持变化图斑高召回率的前提下,尽可能降低变化图斑虚警率,从而提高自然资源要素变化监测效率。为了支撑遥感时空知识图谱智能构建与高效推理,本文设计了顾及时空特性的遥感时空知识图谱本体模式,研发了图数据库内存储运算一体化的GraphGIS工具包。本文提出了基于GraphGIS图数据库原生空间分析的矢量知识抽取技术、基于SkySense视觉大模型高效微调的遥感影像知识抽取技术和基于SeqGPT大语言模型的图斑净化知识抽取技术。在时空本体模式约束下,矢量知识、影像知识和文本知识汇聚形成遥感时空知识图谱。受变化图斑净化业务人工作业方式的启发,本文提出了基于遥感时空知识图谱一阶逻辑推理的变化图斑自动净化技术。为了提升遥感时空知识图谱的并发处理与人机交互核验效率,本文研发了一套遥感时空知识图谱管理服务系统。【结果】针对广东省2024年3—6月自然资源要素变化图斑净化任务,本文方法的存真率达到95.37%、去伪率达到21.82%。【结论】本文提出的自然资源要素变化图斑智能净化算法及系统能够在充分保留真实变化图斑的条件下,可以高效剔除虚警变化图斑,显著提升自然资源要素变化监测作业效率。[Objectives]With the development of deep learning technology,the ability to monitor changes in natural resource elements using remote sensing images has significantly improved.While deep learning change detection models excel at extracting low-level semantic information from remote sensing images,they face challenges in distinguishing land-use type changes from non-land-use type changes,such as crop rotation,natural fluctuations in water levels,and forest degradation.To ensure a high recall rate in change detection,these models often generate a large number of false positive change polygons,requiring substantial manual effort to eliminate these false alarms.[Methods]To address this issue,this paper proposes a natural resource element change polygon purification algorithm driven by remote sensing spatiotemporal knowledge graph.The algorithm aims to minimize the false positive rate while maintaining a high recall rate,thereby improving the efficiency of natural resource element change monitoring.To support the intelligent construction and effective reasoning of the spatiotemporal knowledge graph,this study designed a remote sensing spatiotemporal knowledge graph ontology model taking into account spatiotemporal characteristics and developed a GraphGIS toolkit that integrates graph database storage and computation.This paper also introduces a vector knowledge extraction method based on the native spatial analysis of the GraphGIS graph database,a remote sensing image knowledge extraction method based on efficient fine-tuning of the SkySense visual large model,and a polygon purification knowledge extraction method based on the SeqGPT large language model.Under the constraints of the spatiotemporal ontology model,vector,image,and text knowledge converge to form a remote sensing spatiotemporal knowledge graph.Inspired by the manual operation methods for change polygon purification,this paper developed an automatic purification method of change polygons based on first-order logical reasoning within the knowledge graph.To

关 键 词：时空知识图谱 自然资源要素变化监测 图数据库空间计算 遥感大模型 一阶逻辑推理 遥感影像变化检测 大语言模型 时空智能 

分 类 号：P237[天文地球—摄影测量与遥感] P96[天文地球—测绘科学与技术]