There are many sources of point cloud data, such as the point cloud model obtained after a bundle adjustment of aerial images, the point cloud acquired by scanning a vehicle-borne light detection and ranging (LiDAR), the point cloud acquired by terrestrial laser scanning, etc. Different sensors use different processing methods. They have their own advantages and disadvantages in terms of accuracy, range and point cloud magnitude. Point cloud fusion can combine the advantages of each point cloud to generate a point cloud with higher accuracy. Following the classic Iterative Closest Point (ICP), a virtual namesake point multi-source point cloud data fusion based on Fast Point Feature Histograms (FPFH) feature difference is proposed. For the multi-source point cloud with noise, different sampling resolution and local distortion, it can acquire better registration effect and improve the accuracy of low precision point cloud. To find the corresponding point pairs in the ICP algorithm, we use the FPFH feature difference, which can combine surrounding neighborhood information and have strong robustness to noise, to generate virtual points with the same name to obtain corresponding point pairs for registration. Specifically, through the establishment of voxels, according to the F2 distance of the FPFH of the target point cloud and the source point cloud, the convolutional neural network is used to output a virtual and more realistic and theoretical corresponding point to achieve multi-source Point cloud data registration. Compared with the ICP algorithm for finding corresponding points in existing points, this method is more reasonable and more accurate, and can accurately correct low-precision point cloud in detail. The experimental results show that the accuracy of our method and the best algorithm is equivalent under the clean point cloud and point cloud of different resolutions. In the case of noise and distortion in the point cloud, our method is better than other algorithms. For low-precision point cloud, it can better match the target point cloud in detail, with better stability and robustness.

The registration of long-strip, terrestrial laser scanning (TLS) point clouds is a prerequisite for various engineering tasks, including tunnels, bridges, and roads. An artificial target-based registration method is proposed in this paper to automatically calculate registration parameters (i.e., rotation, translation) of scanned pairs without initial estimations. The approach is based on the well-known Random Sample Consensus (RANSAC) method and effectively searches the point cloud for corresponding returns from a system of artificial targets. In addition, Closed Constraint Adjustment (CCA) is integrated into the registration method to significantly reduce the accumulative error. Experimental results demonstrate the robustness and feasibility of the proposed approach. It is a promising approach to register automatically long strips with limited external control points with satisfactory precision.

Semantic change detection, which aims to locate land cover changes and identify their categories using pixel-level boundaries, has promising applications in Earth vision, including precise urban planning and natural resource management. This paper proposes a novel Siamese U-Net architecture for semantic change detection in synthetic aperture radar (SAR) images, incorporating a residual network with weight-sharing as the backbone network. The network is capable of simultaneously yielding binary change detection and semantic change detection results. Additionally, we have designed a statistic ratio attention module that utilizes statistical features from the original image as spatial ratio attention, coupled with channel attention, to extract change information from the bi-temporal SAR images. Furthermore, as there is currently no existing dataset for semantic change detection in SAR images, we have constructed a dedicated dataset to facilitate model training and evaluation. Our experiment results demonstrate the superiority of our proposed model over other comparison algorithms.

With the rapidly increasing popularization of the automobile, challenges and greater demands have come to the fore, including traffic congestion, energy crises, traffic safety, and environmental pollution. To address these challenges and demands, enhanced data support and advanced data collection methods are crucial and highly in need. A probe-car serves as an important and effective way to obtain real-time urban road traffic status in the international Intelligent Transportation System (ITS), and probe-car technology provides the corresponding solution through advanced navigation data, offering more possibilities to address the above problems. In addition, massive spatial data-mining technologies associated with probe-car tracking data have emerged. This paper discusses the major problems of spatial data-mining technologies for probe-car tracking data, such as true path restoration and the close correlation of spatial data. To address the road-matching issue in massive probe-car tracking data caused by the strong correlation combining road topology with map matching, this paper presents a MapReduce-based technology in the second spatial data model. The experimental results demonstrate that by implementing the proposed spatial data-mining system on distributed parallel computing, the computational performance was effectively improved by five times and the hardware requirements were significantly reduced.