Multi-level Semantic Retrieval Method for Landslide Disaster Data
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摘要: 如何在海量多源多模态的滑坡灾害时空大数据中快速精准地发现满足灾情评估任务需求的优势信息，是综合减灾救灾的关键. 传统灾害数据检索多以“人工经验+关键字”的被动检索方式为主，难以兼顾任务的精确性与时效性，为此，提出了一种面向评估任务的滑坡灾情数据多层级语义检索方法. 通过建立滑坡灾情评估任务对数据特征需求的显式语义描述及任务需求与数据特征之间的高级语义映射，并据此设计多层级语义匹配的数据检索算法，面向灾情评估任务实现优势数据汇聚. 以四川茂县滑坡灾害评估为例进行实验分析，本文检索方法查询效率具有明显优势，900 km2、90 d范围内的灾情数据精准检索效率达到秒级，且推荐优势数据集的准确性高，60 d时间差距阈值下推荐结果平均贴近度达到90%以上. 结果表明本方法可根据任务需求准确可靠地快速自动获取灾害数据，从而显著提高减灾应急响应能力.Abstract: How to quickly and accurately find the superior information to meet the needs of disaster assessment tasks in massive spatio-temporal big data of landslide hazards is the key basis for comprehensive disaster reduction and disaster relief. The traditional disaster data retrieval is mainly based on the passive retrieval method of “artificial experience + keywords”, which makes it difficult to balance the accuracy and timeliness of tasks. This paper proposes a multi-level semantic retrieval method of spatio-temporal data for disaster assessment tasks. By establishing an explicit semantic description of data feature requirements and high-level semantic mapping between task requirements and data features, a multi-level semantic matching data retrieval algorithm is designed to realize superior data aggregation for disaster assessment tasks. Application of the proposed method to the landslide hazard assessment of Maoxian County in Sichuan demonstrates its high query efficiency such as a seconds-level retrieval efficiency in dealing with disaster data in a 900 km2 and 90 day range. The accuracy of the recommended dominant data set is also significant, and the average closeness of the recommended results under the 60-day time gap threshold is over 90%. The results show that the method can quickly and automatically acquire disaster data according to the mission requirements, thus significantly improving the disaster mitigation emergency response capability.
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图 1 面向滑坡灾情评估任务的数据多级需求语义映射
Figure 1. Multi-level requirement semantic mapping of data for landslide disaster assessment tasks
图 3 基于内存直接管理的数据细化筛选流程
Figure 3. Data refinement and filtering process based on direct memory management
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