The electromagnetic radiation (EMR) data collected along a road have a largely empty region overall, while they have a linear distribution locally. Moreover, the traditional spatial interpolation method is not suitable for the electromagnetic radiation space field (EMR-SF) construction collected along the road. In this paper, a layered radial basis function (LRBF) method is proposed to generate the EMR-SF, which interpolates from outside to inside in a layered strategy. First, the regular grid points are constructed based on RBF within the range of sampling data and then are layered based on Ripley’s K function. Second, on the basis of layering, the EMR of grid points is generated layer by layer using the LRBF method. Finally, EMR-SF is constructed by using the sampling data and grid points. The LRBF method is applied to EMR data from an area of Yunnan Normal University in Kunming, China. The results show that the LRBF accuracy is higher than that of the ordinary kriging (OK) and inverse-distance-weighted (IDW) interpolation methods. The LRBF interpolation accuracy can be improved through the strategy of regular grid point construction and layering, and the EMR-SF constructed by LRBF is more realistic than OK and IDW.
Side-slope deformation monitoring compares monitoring data from the same area over different periods and measures the deformation variables. Because of the gaps and coarseness of side-slope monitoring data, a side-slope monitoring method that integrates terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV)–based photogrammetry point clouds is proposed, aiming to solve the problem of slope monitoring in complex scenes. First, TLS and UAV-based photogrammetry point clouds are acquired. Then, the two types of point clouds are registered by an iterative closest point algorithm. Next, the data gap areas in the TLS point cloud are detected, and a gap-filling method is used to integrate the UAV-based photogrammetry point cloud with the TLS point cloud. Finally, side-slope deformation is detected based on a multiscale model-to-model cloud comparison algorithm. A side slope in Chenggong, Kunming, China, is taken as an example. The surface deformation of the side slope was monitored during January and June 2021. The experimental results show that the registration errors of the two-phase integration point cloud are 0.039 m and 0.035 m. The root mean square errors of the four ground checkpoints are 0.033 m and 0.038 m. Finally, the side slope is found to have deformed and formed a main deformation area, which shows that this side slope was in an active state.
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