Prediction of the Deformation of Heritage Building Communities under the Integration of Attention Mechanisms and SBAS Technology

Abstract

The protection of heritage building communities is of important historical significance, the occurrence of a landslide is related to the safety and stability of the heritage building, and ground monitoring and forecasting are the key steps for the early warning and timely restoration of the heritage building. This study utilizes remote sensing technology to monitor the ground of a cultural heritage building, and employs a Long Short-Term Memory (LSTM) network for prediction. Firstly, we conducted ground subsidence monitoring within a specific time series of the study area using heritage remote sensing images and SBAS-InSAR technology. Following the subsidence monitoring, and incorporating an attention mechanism, we effectively localized and extracted features of heritage building clusters within the region. This approach efficiently addresses the challenge of feature identification resulting from the dense distribution of buildings and the similarity between various objects. The results indicate that the maximum subsidence rate in the research area reached −60 mm/year, reached a maximum uplift rate of 45 mm/year, and that the maximum cumulative subsidence reached −65 mm. Secondly, for the multi-level, multi-scale, and class-specific objects in remote sensing images, the LSTM network enables adaptive contextual information during deep and shallow feature extraction. This allows for better contextual modeling and the correlation between predicted and actual results reaches a 0.95 correlation, demonstrating the accurate predictive performance of the LSTM network. In conclusion, both LSTM and SBAS technologies play a crucial role in decision-making for heritage buildings, facilitating effective early warning and disaster mitigation.

Analysis and Prediction of Urban Surface Transformation Based on Small Baseline Subset Interferometric Synthetic Aperture Radar and Sparrow Search Algorithm–Convolutional Neural Network–Long Short-Term Memory Model

“…The SBAS-InSAR technique [ 21 , 22 , 23 , 24 ] performs deformation measurements by means of a small baseline differential interferometric atlas, which can reduce the influence of spatial de-correlation and terrain errors, and then applies the singular value decomposition (SVD) method based on the least-paradigm criterion of the deformation rate to obtain the deformation rate of coherent targets and their time series [ 25 ]. Its technological flow is shown in Figure 2 .…”

“…Sensors 2024, 24 (1) Data preparation and analysis stage: The SBAS-InSAR technique was used to a time series surface deformation data in the study area. The air temperature, h ity, precipitation, and ground temperature at 5 cm, 10 cm, and 15 cm below th face in the study area were used as input data, and the time series cumulative s deformation data at the feature points were used as the output.…”