Dynamic evolution of remote sensing information service chain driven by earthquake damage assessment
Natural disasters inflict severe damage on social and economic development,posing a significant barrier to national economic progress.Among these,earthquakes stand out as one of the most unpredictable and sudden natural disasters,underscoring the critical importance of disaster assessment.Rapid and accurate assessment of such events can furnish essential technical support and a decision-making foundation for post-disaster emergency rescue,command decisions,and reconstruction efforts.Given the immature state of global research on earthquake occurrences,accurately predicting the timing of earthquakes remains a formidable challenge.Consequently,timely access to information regarding the condition of buildings,infrastructure,lifelines,farmland,and other vulnerable elements is imperative for effective emergency rescue and disaster relief operations.In recent years,remote sensing technology has demonstrated considerable potential in disaster prevention,mitigation,and relief efforts,emerging as a cornerstone of modernizing disaster prevention and mitigation in China.It provides robust support for disaster monitoring,evaluation,emergency response,and command decision-making.As global spatial infrastructure enters a new phase of systematic development and global service provision,the rapid advancement of new remote sensing satellites,such as the Gaofen(GF)satellite series,propels the transformation of China's disaster remote sensing services towards business-oriented offerings.The evolution of service chains involves restructuring existing service chains or adjusting individual services based on dynamic environmental changes or evolving task requirements to meet new demands.The task-driven dynamic evolution of remote sensing information services for earthquake damage refers to the continuous adjustment and optimization of remote sensing information service chains in response to the requirements and changes of earthquake disaster assessment tasks.This approach focuses on dynamically adapting and enhancing service chains according to specific earthquake disaster assessment task requirements to address the constantly changing environment and task demands.Through task-driven methodologies,remote sensing information service chains can flexibly respond to various earthquake disaster assessment tasks and update or optimize service chains as needed to ensure more effective support and solutions during disaster response.Currently,existing spatial information service chains in the field of disaster emergency response predominantly rely on traditional"metadata+human expertise"service chain models.These models struggle to accommodate the dynamic semantics of seismic damage requirements,the richness of data semantics,the complexity of remote sensing information processing semantics,and the interrelationships between different semantics.Therefore,there is an urgent need to study remote sensing information service chain evolution technologies tailored for earthquake damage assessment,ensuring alignment with changing task requirements and compatibility among data,models,and algorithms.This article proposes a dynamic evolution method for service chains based on reasoning algorithms,taking into account the relationships between tasks,data,and services comprehensively.With an evolution time of less than 10 minutes,this method demonstrates its capability to provide timely,stable,and effective support for earthquake disaster assessment.
NETL
NSTL
万方数据
