Research on Intelligent Risk Identification Model of Immovable Sites Driven by Heterogeneous Sensing Data of Satellites

The conservation and safeguarding of immovable cultural heritage sites is an urgent issue of international concern especially in China, which has geological activities and climate sensitive areas. Conventional monitoring tools are known to be expensive, low temporal resolution, and small spatial coverage, which makes them unable to offer real-time and comprehensive risk-assessment on large site networks. In this research an apt risk identification model of the immovable heritage sites under the influence of heterogeneous satellite measurements will be presented and this shall incorporate the multi-source remote sensing imagery such as synthetic aperture radar (SAR), multispectral optical data, and thermal infrared data. The suggested framework is based on an objectoriented analytical paradigm which conceptualizes individual monitored locations as Digital Site Object (DSO), and considers individual monitored sites to be a first-class data unit that can be accessed and operated in a single geospatial information infrastructure. The model finds a hierarchical feature extraction pipeline, which is integrated with convolutional neural networks (CNNs) and transformerbased attention patterns to detect structural deformation, surface subsidence, vegetation encroachment, and water infiltration abnormalities. Multi-temporal change detection algorithm is used on time-series satellite measurements which provide the ability to quantify the change of risk on the four major dimensions: geological instability, climatic exposure, anthropogenic disturbance, and structural vulnerability. Data on 186 nationally registered immovable heritage sites within the Loess Plateau region in Shaanxi Province and karst landscape in Guizhou Province in China were used to validate the model. Experimental data show that the proposed model has a total risk classification rate of 91.4% and a Kappa coefficient of 0.886, surpassing those of baseline methods such as single-source assessment and traditional machine learning methods. The experiment also reveals that heterogeneous data fusion could greatly improve the latent risk detection that would not have been detected by any of the sensors. Such results offer an empirical and extensively generalizable approach to the monitoring of heritage sites on the national level and a theoretical approach to the inclusion of intelligent remote sensing systems as part of the cultural heritage management technologies.