Under the background of rapid urban development and continuous climate change, frequent floods around the world have caused serious economic losses and social problems, which has become the main reason for the sustainable development of cities. Flood disaster risk assessment is an important non-engineering measure in urban disaster prevention and mitigation, and scientific flood disaster risk assessment is the premise and foundation of flood disaster risk management. This paper summarizes the current situation of flood risk assessment by analyzing the international literature in recent 20 years. The mechanism of flood disaster is mainly discussed. The flood disaster assessment methods are summarized, including historical disaster statistics method, multi-criteria index system method, remote sensing and GIS (Geographic Information System) coupling method, scenario simulation evaluation method and machine learning method. Furthermore, the development status of flood risk analysis and forecasting is summarized. Finally, the development trend and direction of flood risk assessment are put forward.

The image will be contaminated by noise during the imaging process, which severely degrades the image quality. It is necessary to filter the collected image. With the increasing amount of image data, the traditional single-processor or multiprocessor computing equipment has been unable to meet the requirements of real-time data processing. In this paper, the computational model of weighted mean filtering and the characteristics of high performance computer architecture are studied. An efficient hierarchical image weighted mean filtering parallel algorithm for Open Computing Language (OpenCL) is designed and implemented, which can fully express the parallelism of the computing model. The parallel algorithm takes full account of the characteristics of image discrete convolution computing and the multilayer logic architecture of high performance computer, deeply excavates the parallelism of the computing platform and computing model, and realizes the efficient task mapping from computing model to computing resources. The model is implemented in parallel with the two levels of work-group and workitem. The experimental results show that compared with the serial algorithm based on CPU, the parallel algorithm based on Open Multi-Processing (OpenMP) and the parallel algorithm based on Compute Unified Device Architecture (CUDA), the parallel algorithm of weighted mean filtering achieves 20.88 times, 18.52 times and 1.26 times acceleration ratio on the NVIDIA GPU computing platform based on OpenCL architecture, respectively. It realizes better computing performance and runs on different Graphic Processing Unit (GPU) computing platforms, and has good portability and scalability.INDEX TERMS weighted mean filtering; Gaussian noise; Graphic Processing Unit (GPU); Open Computing Language (OpenCL); parallel algorithm.

Unmanned aerial vehicle (UAV) inspection of vegetation or other obstacles in power transmission line corridors is an economical and efficient method. Spatial power lines cannot be directly restored in the single airborne photographic geometry, and automatic robust extraction of power lines under complex background is a bottleneck problem in the current transmission line reconstruction. Thus, a robust restoration method of the power line spatial position based on the vertical plane constraint was proposed that can restore the entire power line threedimensional (3D) points. First, the spatial position of the hanging points at both ends of the power line was solved based on an insulator plumb, and the vertical plane of the power line was determined. A light beam passing through the photographing centre and the image point could then be determined for any image point on the image power line. The intersection point of the light beam and the vertical plane of the power line was the point of the spatial power line. The proposed method was used to select a few (three or more) power line seed points manually to calculate the corresponding 3D space points and then fit these points to obtain the dense and uniform 3D points of power line. After restoring the Ó 2022 Remote Sensing and Photogrammetry Society and John Wiley & Sons Ltd. spatial power line, the minimum distance error line segment based on splitting was used to approximate the 3D point sequence of the power line, and it was divided into several straight-line segments. The spatial buffer zone was established with each line segment as the centre and a safety distance as the radius to detect possible obstacles in the power line corridor. Finally, a set of simulated and actual data were used for verification and analysis. Results show that the method can effectively detect obstacles around the power line, and the accuracy is within 0.25 m.

Due to the memory limitation and lack of computing power of consumer level computers, there is a need for suitable methods to achieve 3D surface reconstruction of large-scale point cloud data. A method based on the idea of divide and conquer approaches is proposed. Firstly, the kd-tree index was created for the point cloud data. Then, the Delaunay triangulation algorithm of multicore parallel computing was used to construct the point cloud data in the leaf nodes. Finally, the complete 3D mesh model was realized by constrained Delaunay tetrahedralization based on piecewise linear system and graph cut. The proposed method performed surface reconstruction on the point cloud in the multicore parallel computing architecture, in which memory release and reallocation were implemented to reduce the memory occupation and improve the running efficiency while ensuring the quality of the triangular mesh. The proposed algorithm was compared with two classical surface reconstruction algorithms using multigroup point cloud data, and the applicability experiment of the algorithm was carried out; the results verify the effectiveness and practicability of the proposed approach.

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