大型语言模型驱动的知识图推理用于增强语义细分的语义细分
Jinhe Su1, Xiaorong Zhang1, Yang Luo1
1The School of Computer Engineering, Jimei University, Xiamen, 361021, China.
Scientific reports
|December 10, 2025
概括
本研究引入了使用大型语言模型 (LLM) 创建用于遥感场景细分的通用知识图的新框架. 这提高了各种城市,农村和港口环境的适应性和稳定性.
科学领域:
- 地理空间人工智能 人工智能
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
背景情况:
- 城市场景细分对于3D城市建模和城市规划等遥感应用至关重要.
- 由于数据集特定的知识图,现有的方法难以泛化.
研究的目的:
- 开发一种新的远程传感语义细分框架,使用通用知识图.
- 为了提高城市场景的适应性和稳定性,在各种数据源中了解城市场景.
主要方法:
- 利用大型语言模型 (LLM) 来从多来源的地理空间数据中构建一个通用知识图.
- 实现图形构造模块,用于提取跨域语义关系.
- 使用知识图融合模块 (KGFusion) 将图集成到语义细分网络中.
主要成果:
- 在UAVid数据集上实现了70.94%的mIoU,在混合数据集上达到63.23%.
- 比基线方法的表现分别高出0.43%和1.04%.
- 在跨领域场景中证明了效率和适应性.
结论:
- 拟议的框架有效地增强了遥感语义细分,使用通用知识图.
- 该方法显示出显著的稳定性和在复杂的城市环境中更广泛应用的潜力.
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