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国土调查是支撑国家自然资源管理的重要基础性工作，遥感技术凭借其宏观、动态、高效的优势成为核心数据来源。然而，海量多源异构遥感数据的处理仍面临效率低、精度不足、自动化程度有限等瓶颈。本文针对国土调查的实际需求，系统梳理遥感数据智能处理的技术体系与挑战，重点分析数据预处理、信息智能提取、动态变化监测三大环节的关键问题，并提出融合人工智能、云计算与流程优化的综合解决方案。通过构建自动化数据预处理流水线、设计面向地类解译的深度神经网络模型、引入时序分析强化动态监测能力，并结合高性能计算平台实现资源调度优化。实验表明，该方法显著提升了数据处理效率与解译精度，有效支撑了高分辨率、高频次国土调查的实施，为自然资源精细化管理提供可靠技术支撑。全流程智能化改造是提升国土调查效能的关键路径。

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