Zhao Xiangyu, Zhang Chunju, Luo Chenchen, Zhang Jun, Chu Chaoqun, Li Chenxi, Pei Yifan, Wu Zhaofu
College of Civil Engineering, Hefei University of Technology, Hefei 230009, China.
National Geomatics Center of China, Beijing 100830, China.
Sensors (Basel). 2025 Sep 6;25(17):5575. doi: 10.3390/s25175575.
To address the challenges of complex task matching, limited semantic representation, and low recommendation efficiency in remote sensing data acquisition for natural disasters, this study proposes a semantic path-guided recommendation method based on a knowledge graph framework. A disaster-oriented remote sensing knowledge graph is constructed by integrating entities such as disaster types, remote sensing tasks, observation requirements, sensors, and satellite platforms. High-order meta-paths with semantic closure are designed to model task-resource relationships structurally. A Meta-Path2Vec embedding mechanism is employed to learn vector representations of nodes through path-constrained random walks and Skip-Gram training, capturing implicit semantic correlations between tasks and sensors. Cosine similarity and a Top-K ranking strategy are then applied to perform intelligent task-driven sensor recommendation. Experiments on multiple disaster scenarios-such as floods, landslides, and wildfires-demonstrate the model's high accuracy and robust stability. An interactive recommendation system is also developed, integrating data querying, model inference, and visual feedback, validating the method's practicality and effectiveness in real-world applications. This work provides a theoretical foundation and practical solution for intelligent remote sensing data matching in disaster contexts.
为应对自然灾害遥感数据采集过程中复杂任务匹配、语义表示有限和推荐效率低下等挑战，本研究提出了一种基于知识图谱框架的语义路径引导推荐方法。通过整合灾害类型、遥感任务、观测要求、传感器和卫星平台等实体，构建了面向灾害的遥感知识图谱。设计了具有语义封闭性的高阶元路径，从结构上对任务-资源关系进行建模。采用Meta-Path2Vec嵌入机制，通过路径约束随机游走和Skip-Gram训练来学习节点的向量表示，捕捉任务与传感器之间的隐含语义关联。然后应用余弦相似度和Top-K排序策略进行智能任务驱动的传感器推荐。在洪水、山体滑坡和野火等多种灾害场景下的实验表明，该模型具有较高的准确性和强大的稳定性。还开发了一个交互式推荐系统，集成了数据查询、模型推理和视觉反馈，验证了该方法在实际应用中的实用性和有效性。这项工作为灾害环境下的智能遥感数据匹配提供了理论基础和实际解决方案。
