RTVis: Research Trend Visualization Toolkit

When researchers and practitioners are about to start a new project or have just entered a new research field, choosing a proper research topic is always challenging. To help them have an overall understanding of the research trend in real-time and find out the research topic they are interested in, we develop the Research Trend Visualization toolkit (RTVis) to analyze and visualize the research paper information. RTVis consists of a field theme river, a co-occurrence network, a specialized citation bar chart, and a word frequency race diagram, showing the field change through time respectively, cooperating relationship among authors, paper citation numbers in different venues, and the most common words in the abstract part. Moreover, RTVis is open source and easy to deploy. The demo of our toolkit and code with detailed documentation are both available online.Comment: Work submitted to IEEE VIS 2023 (Poster). 2 pages, 1 figure. For our demo page, visit http://www.rtvis.design

Application of the Periodic Average System Model in Dam Deformation Analysis

Dams are among the most important hydraulic engineering facilities used for water supply, flood control, and hydroelectric power. Monitoring of dams is crucial since deformation might have occurred. How to obtain the deformation information and then judge the safe conditions is the key and difficult problem in dam deformation monitoring field. This paper proposes the periodic average system model and creates the concept of “settlement activity” based on the dam deformation issue. Long-term deformation monitoring data is carried out in a pumped-storage power station, this model combined with settlement activity is used to make the single point deformation analysis, and then the whole settlement activity profile is drawn by clustering analysis. Considering the cumulative settlement value of every point, the dam deformation trend is analyzed in an intuitive effect way. The analysis mode of combined single point with multipoints is realized. The results show that the key deformation information of the dam can be easily grasped by the application of the periodic average system model combined with the distribution diagram of settlement activity. And, above all, the ideas of this research provide an effective method for dam deformation analysis

Prediction of university fund revenue and expenditure based on fuzzy time series with a periodic factor.

Financial management and decision-making of universities play an essential role in their development. Predicting fund revenue and expenditure of universities can provide a necessary basis for funds risk prevention. For the lack of solid data reference for financial management and funds risk prevention in colleges and universities, this paper presents a prediction model of University fund revenue and expenditure based on fuzzy time series with a periodic factor. Combined with the fuzzy time series, this prediction method introduces the periodic factor of university funds. The periodic factor is used to adjust the proportion of the predicted value of the fuzzy time series and the periodic observation value. A fund revenue prediction model and a fund expenditure prediction model are constructed, and an experiment is carried out with the actual financial data of a university in China. The experimental result shows the effectiveness of the proposed model, which can provide solid references for financial management and funds risk prevention in universities

Integrating OpenStreetMap tags for efficient LiDAR point cloud classification using graph neural networks

ABSTRACTThe urban environment exhibits significant vertical variations, Light Detection and Ranging (LiDAR) point cloud classification can provide insights for the 3D morphology of the urban environment. Introducing the adjacency relationships between urban objects can enhance the accuracy of LiDAR point cloud classification. Graph Neural Network (GNN) is a popular architecture to infer the labels of urban objects by utilizing adjacency relationships. However, existing methods ignored the power of the known labels of urban objects, such as crowd-sourced tagged labels from OpenStreetMap (OSM) data, in the inferring process. Therefore, this study proposes a strategy introduces OSM data into GNN for LiDAR point cloud classification. First, we perform an over-segmentation of the LiDAR point cloud to obtain superpoints, which act as basic elements for constructing superpoint adjacency graphs. Second, PointNet is applied to embed superpoint features and edge features are generated using these superpoint features. Finally, OSM data is associated with some part of superpoints and incorporated into the GNN to update the embedded features of superpoints. The results demonstrate that the GNN with OSM data significantly improves the classification accuracy of original GNN. The improvement highlights taking advantage of crowd-sourced geoinformation in LiDAR point cloud classification for understanding 3D urban landscape

An automatic framework for pylon detection by a hierarchical coarse-to-fine segmentation of powerline corridors from UAV LiDAR point clouds

This paper proposes an automatic framework for pylon detection by a hierarchical coarse-to-fine segmentation of powerline corridors from UAV laser scanning point clouds. To this end, the proposed framework starts by roughly detecting the pylon location using the voxel-based height features derived from powerline corridor distribution in the vertical direction. The roughly detected pylons are then fed into the fine-grained pylon segmentation step, from which the fine-grained pylon points are learned by leveraging the shape prior knowledge. The idea behind the fine-grained is that most of the pylons can be cut horizontally into a series of rectangular cross-sections whose sizes from top to bottom are growing at a constant rate. By this linear growth relationship, the distorted cross-sections, which most commonly occur at pylon legs due to the influence of the attachments, such as trees and brush, can be accurately restored using the linear least squares regression. The performance of the proposed method was evaluated on two datasets over hilly and flat landforms. Our evaluation results showed that for powerlines in flat terrain, the proposed method achieved a precision of 99.8%, recall of 99.5%, and F1-score of 99.7%. On hilly terrain, a slightly lower performance was obtained, with a precision of 98.8%, recall of 97.8%, and F1-score of 98.3%. The proposed method’s accuracy is on par with or even better than other mainstream pylon detection algorithms

Coastal bathymetry inversion using SAR-based altimetric gravity data: A case study over the South Sandwich Island

The global bathymetry models are usually of low accuracy over the coastline of polar areas due to the harsh climatic environment and the complex topography. Satellite altimetric gravity data can be a supplement and plays a key role in bathymetry modeling over these regions. The Synthetic Aperture Radar (SAR) altimeters in the missions like CryoSat-2 and Sentinel-3A/3B can relieve waveform contamination that existed in conventional altimeters and provide data with improved accuracy and spatial resolution. In this study, we investigate the potential application of SAR altimetric gravity data in enhancing coastal bathymetry, where the effects on local bathymetry modeling introduced from SAR altimetry data are quantified and evaluated. Furthermore, we study the effects on bathymetry modeling by using different scale factor calculation approaches, where a partition-wise scheme is implemented. The numerical experiment over the South Sandwich Islands near Antarctica suggests that using SAR-based altimetric gravity data improves local coastal bathymetry modeling, compared with the model calculated without SAR altimetry data by a magnitude of 3.55 m within 10 km of offshore areas. Moreover, by using the partition-wise scheme for scale factor calculation, the quality of the coastal bathymetry model is improved by 7.34 m compared with the result derived from the traditional method. These results indicate the superiority of using SAR altimetry data in coastal bathymetry inversion

A Deep Learning Based Method for Railway Overhead Wire Reconstruction from Airborne LiDAR Data

Automatically and accurately reconstructing the overhead wires of railway from airborne laser scanning (ALS) data are an efficient way of railway monitoring to ensure stable and safety transportation services. However, due to the complex structure of the overhead wires, it is challenging to extract these wires using the existing methods. This work proposes a workflow for railway overhead wire reconstruction using deep learning for wire identification collaborating with the RANdom SAmple Consensus (RANSAC) algorithm for wire reconstruction. First, data augmentation and ground points down-sampling are performed to facilitate the issues caused by insufficient and non-uniformity of LiDAR points. Then, a network incorporating with PointNet model is proposed to segment wires, pylons and ground points. The proposed network is composed of a Geometry Feature Extraction (GFE) module and a Neighborhood Information Aggregation (NIA) module. These two modules are introduced to encode and describe the local geometric features. Therefore, the capability of the model to discriminate geometric details is enhanced. Finally, a wire individualization and multi-wire fitting algorithm is proposed to reconstruct the overhead wires. A number of experiments are conducted using ALS point cloud data of railway scenarios. The results show that the accuracy and MIoU for wire identification are 96.89% and 82.56%, respectively, which demonstrates a better performance compared to the existing methods. The overall reconstruction accuracy is 96% over the study area. Furthermore, the presented strategy also demonstrated its applicability to high-voltage powerline scenarios

Semantic-aware room-level indoor modeling from point clouds

This paper introduces a framework for reconstructing fine-grained room-level models from indoor point clouds. The motivation behind our method stems from the consistent floorwise appearance of building shapes in urban buildings along the vertical direction. To this end, each floor’s points are horizontally sliced to obtain a representative cross-section, from which the linear primitives are detected and enhanced. These linear primitives help to divide the entire space into non-overlapping connected faces with shared edges. These faces are then classified as indoor or outdoor categories by solving a binary energy minimization formulation. The indoor faces are further grouped into each individual rooms with the support of the room semantic map. By propagating and tracing each room’s contour, 2D floor plan can be generated in a semantic-aware manner. These generated 2D floor plans are vertically stretched to match the heights of their respective rooms. Experimental results on six complex scenes from the S3DIS dataset, which encompass both linear and non-linear shapes, demonstrate that our created room models exhibit accurate geometry, correct topology, and rich semantics. The source code of our room-level modeling algorithm is available at https://github.com/indoor-modeling/indoor-modeling