A method for building a remote sensing information service chain driven by earthquake damage assessment tasks
Earthquakes represent one of the most catastrophic disasters globally,causing havoc and devastation in affected regions.The use of remote sensing data for dynamic earthquake damage assessment is vital for emergency response,reconstruction efforts,and mitigating disaster impacts to some extent.However,the field of disaster management is rapidly evolving with advancements in Web services and online computing technologies,necessitating an intelligent approach to selecting suitable remote sensing data and processing services to establish a robust remote sensing information service chain.This emerging challenge is crucial given the diverse range of seismic hazard assessment tasks that require tailored solutions.The combination of services based on static workflows heavily relies on domain experts'knowledge to design processes,demanding a high background in relevant field knowledge.Real-time adaptability is challenging when earthquakes occur,limiting the flexibility and dynamism of services.On the other hand,AI-based service combinations demonstrate strong automation and good dynamism to meet dynamic business needs.However,implementation and problem-solving algorithms pose difficulties and time-consuming challenges due to inadequate training samples,resulting in relatively opaque decision-making processes that require enhancement.Semantic matching and reasoning-based service combinations can dynamically generate change detection and processing service chains but face technical barriers that hinder large-scale rapid responses,with current research on service combinations applied in disaster fields being limited.In response to this urgent requirement and problem above,we introduce a task-driven methodology tailored for constructing a remote sensing information service chain.This methodology stems from a thorough analysis of earthquake damage tasks and the landscape of remote sensing services,culminating in the development of a unified description semantic model for earthquake damage assessment tasks and a corresponding remote sensing information service description model based on ontology.The essence of this approach lies in its ability to bridge the gap between specific task requirements and the available suite of remote sensing services,ensuring a harmonious alignment between needs and solutions.A key highlight of our study is the exploration of a multi-level semantic matching method aimed at refining services within the constructed service chains.This refinement process delves into three critical dimensions:service type,function,and quality,enabling a nuanced evaluation and selection process.Our research exemplifies the efficiency and effectiveness of this methodology by successfully constructing twelve service chains within a remarkably short span of 70 minutes.The achieved optimal normalized quality of service(QoS)index of 0.607 highlights the superior performance and precision of the constructed service chains.The experimental findings underscore the strength and applicability of the proposed method in facilitating the construction of remote sensing information service chains.The quantitative rigor embedded in the results enhances the method's credibility and underscores the tangible benefits it provides in optimizing earthquake damage assessment processes.
NETL
NSTL
万方数据
