Article
Engineering, Multidisciplinary
Bo Liu, Dingxuan Zhao, Jinming Chang, Shuangji Yao, Tao Ni, Mingde Gong
Summary: This study proposes an algorithm for constructing a real-time terrain model from LiDAR points, which can be used for preview control. The model includes elevation maps, feature detection, and confidence intervals, integrating reliability and geometric features using statistical and signal processing techniques. Experimental results show that the method is competitive in terms of precision and efficiency compared to state-of-the-art methods.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2022)
Article
Environmental Sciences
Huxiong Li, Weiya Ye, Jun Liu, Weikai Tan, Saied Pirasteh, Sarah Narges Fatholahi, Jonathan Li
Summary: This study introduces a novel workflow for automated Digital Terrain Model (DTM) extraction from Airborne LiDAR point clouds based on a convolutional neural network and transfer learning. The results demonstrate that the proposed workflow establishes a superior DTM extraction accuracy with a root mean square error of only 7.3 cm for the interpolated DTM at 1 m resolution.
Article
Engineering, Multidisciplinary
Chuanfa Chen, Jiaojiao Guo, Yanyan Li, Lianzhong Xu
Summary: In order to improve the accuracy of filtering in complex environments, this paper proposes a segmentation-based hierarchical interpolation filter that utilizes both geometric and radiometric features. The method involves segmenting the raw point cloud using DBSCAN and selecting initial ground seeds based on terrain features. Ground points are then detected using an enhanced multiresolution hierarchical filter with three reference ground surfaces and slope-adaptive thresholds. Experimental results demonstrate that the proposed method outperforms state-of-the-art filtering methods, achieving significant reductions in average type I, II, and total errors, as well as an improvement in the kappa coefficient.
Article
Environmental Sciences
Fayez Tarsha Kurdi, Zahra Gharineiat, Glenn Campbell, Mohammad Awrangjeb, Emon Kumar Dey
Summary: This paper presents a new algorithm for automatic building point cloud filtering based on the Z coordinate histogram, dividing the building point cloud into three zones and recognizing high tree crown points by analyzing normal vectors and the curvature factor. The suggested approach was tested on five datasets with different point densities and urban typology, showing high efficacy with accuracy values of 97.9%, 97.6%, and 95.6%.
Article
Environmental Sciences
Bowen Li, Hao Lu, Han Wang, Jianbo Qi, Gang Yang, Yong Pang, Haolin Dong, Yining Lian
Summary: In recent years, there has been increasing interest in using LiDAR technology on unmanned aerial vehicles (UAVs) to capture the 3D structure of forests for forestry and ecosystem monitoring. This study proposed a highly efficient network called Terrain-net, which combines 3D point convolution and self-attention mechanism to filter non-ground LiDAR points in forested environments. The network achieved the best performance in terms of accuracy and was able to transfer well to other vegetated environments.
Article
Remote Sensing
Li He, Yong Pang, Zhongjun Zhang, Xiaojun Liang, Bowei Chen
Summary: ICESat-2, equipped with a photon-counting LiDAR system called ATLAS, is collecting earth elevation data worldwide, showing great potential for large-scale forest monitoring. However, the low energy emission and noise interference make accurate photon classification necessary. Researchers proposed an improved local outlier factor algorithm with a rotating search area (LOFR) to address the limitations of existing algorithms in complex terrain areas.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
Article
Computer Science, Software Engineering
Volkan Yilmaz
Summary: The majority of ground filtering techniques rely on user-defined parameter values, making it difficult to achieve optimum performance in areas with abrupt topography changes. Utilizing machine learning algorithms for ground filtering could be a more efficient strategy in such areas. Evaluations showed that SVM-based ground filtering approach achieved the best result, outperforming ML, RF, and ANN methods.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2021)
Article
Environmental Sciences
Felipe Lima Ramos Barbosa, Renato Fontes Guimaraes, Osmar Abilio de Carvalho Junior, Roberto Arnaldo Trancoso Gomes, Osmar Luiz Ferreira de Carvalho, Thyego Pery Monteiro de Lima
Summary: LiDAR is a useful source of elevation data, and GEDI is a spaceborne system that provides accurate ground elevation information. However, GEDI data has noise due to atmospheric conditions and shows significant differences compared to reference data. This research uses the Kolmogorov-Smirnov test to define a threshold and selects samples with lower RMSE values, resulting in more accurate GEDI data.
Article
Engineering, Multidisciplinary
Haohai Fu, Huamin Yang, Chunyi Chen
Summary: This paper presents a real-time rendering algorithm for large-scale terrain based on GPU tessellation, with a new rule in the tessellation stage to define terrain roughness.
ALEXANDRIA ENGINEERING JOURNAL
(2021)
Article
Environmental Sciences
Hunsoo Song, Jinha Jung
Summary: Digital terrain model (DTM) creation is a modeling process that represents the Earth's surface. An aptly designed DTM generation method tailored for intended study can significantly streamline ensuing processes and assist in managing errors and uncertainties, particularly in large-area projects. We introduce a new DTM generation method that performs object-based ground filtering, which is particularly beneficial for urban topography, offering unique features and high accuracy.
Article
Geography, Physical
Chuanfa Chen, Yixuan Bei, Yanyan Li, Weiwei Zhou
Summary: This study evaluated the performance of five interpolation methods for quantifying Terrain Surface Roughness (TSR) under different LiDAR data densities. Results show that Thin Plate Spline (TPS) consistently outperforms other methods in accuracy, while Inverse Distance Weighting (IDW) produces the worst results. Additionally, reducing data density to 50% does not significantly impact the accuracy of DEMs.
Article
Environmental Sciences
Cynthia L. Norton, Kyle Hartfield, Chandra D. Holifield Collins, Willem J. D. van Leeuwen, Loretta J. Metz
Summary: Mapping the spatial distribution of woody vegetation in semi-arid regions is challenging due to the similarities between tree species and the small and sparse canopy cover. This study investigates the use of multi-temporal, airborne hyperspectral imagery and LiDAR data for tree species classification, achieving high accuracies by combining fine spatial resolution data and employing a reproducible scripting and machine learning approach.
Article
Geosciences, Multidisciplinary
Aubrey Miller, Pascal Sirguey, Simon Morris, Perry Bartelt, Nicolas Cullen, Todd Redpath, Kevin Thompson, Yves Buhler
Summary: Accurate digital elevation models (DEMs) are crucial for natural hazard models. This study uses satellite photogrammetry and topographic lidar to generate high-resolution DEMs and investigates the sensitivity of simulation results to the source and spatial resolution of the DEM.
NATURAL HAZARDS AND EARTH SYSTEM SCIENCES
(2022)
Article
Environmental Sciences
Zhenyang Hui, Zhuoxuan Li, Dajun Li, Yanan Xu, Yuqian Wang
Summary: This paper proposes a self-adaptive filtering method based on object primitive global energy minimization for processing large-scale and complicated urban environments in airborne LiDAR datasets. By generating a mode graph and defining an energy function based on it, the filtering process is transformed into iterative global energy minimization. Experimental results show that the developed filter outperforms three classical filtering methods in terms of total error and Kappa coefficient.
Article
Environmental Sciences
Libo Cheng, Rui Hao, Zhibo Cheng, Taifeng Li, Tengxiao Wang, Wenlong Lu, Yulin Ding, Han Hu
Summary: This study focuses on the challenges of ground filtering in large scenes and introduces an elevation offset-attention (E-OA) module that considers global semantic features and integrates them into existing network frameworks. The experimental results demonstrate that this module significantly improves the ground filtering performance and surpasses traditional methods and other competing attention frameworks.
Article
Geochemistry & Geophysics
Chen Liao, Xiangyun Hu, Shihui Zhang, Xuewen Li, Quanzeng Yin, Zhao Zhang, Longfei Zhang
Summary: Joint inversion is an effective technique for obtaining high-resolution solutions in geophysics. In this study, a new inversion strategy based on the alternating direction method of multipliers was proposed for convenient and efficient joint inversion. Three optimization algorithms were presented for different types of geophysical data, resulting in more accurate results compared to separate inversions. The algorithms were successfully applied to field data, verifying the practicality of the inversion strategy.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2022)
Article
Geochemistry & Geophysics
Qianguo Yang, Xiangyun Hu, Shuang Liu, Qu Jie, Huaijiang Wang, Qiuhua Chen
Summary: A gravity inversion approach based on convolutional neural networks is proposed in this letter, demonstrating good results in comparison with other inversion methods in synthetic data tests.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Song Jin, Alexey Stovas, Xiangyun Hu
Summary: Reflection moveout approximations are commonly used tools in seismic data modeling and inversion. We developed a new parameterization for the generalized moveout approximation (GMA) and proposed a moveout inversion method for layered acoustic VTI media. Numerical tests showed that the accuracy of the proposed GMA method is influenced by the location of the middle reference offset.
Article
Geochemistry & Geophysics
Ronghua Peng, Bo Han, Yajun Liu, Xiangyun Hu
Summary: A quantitative assessment of model parameter uncertainty is crucial for a reliable interpretation of electromagnetic data. The Bayesian inference framework provides an effective way to rigorously estimate parameter uncertainty related to the recovered solution. The adaptive nature of the reversible jump Markov chain Monte Carlo algorithm allows for inferring the appropriate level of model complexity and associated parameter uncertainty through data.
Article
Astronomy & Astrophysics
Jing-Hui Huang, Xue-Ying Duan, Chen Huan, Guang-Jun Wang, Xiang-Yun Hu
Summary: Operator-product expansion is used to obtain the nonlocal quark scalar condensate component of gluon vacuum polarization. The gluon propagator is determined and shown to be finite in the infrared domain, with the single gluon mass mg being determined. The gluon Schwinger function analysis finds evidence of positivity violations in the gluon propagator.
MODERN PHYSICS LETTERS A
(2022)
Article
Environmental Sciences
Tianya Luo, Xiangyun Hu, Longwei Chen, Guilin Xu
Summary: Considering the inherent anisotropy of the earth is crucial for interpreting magnetotelluric (MT) data. In this study, we used the edge-based finite element method to calculate the responses of MT data for electrical isotropic and anisotropic models. We then used the anisotropy index and polar plot to depict MT responses, which helped differentiate isotropy from anisotropy and reveal the directions of principal conductivities.
Article
Geochemistry & Geophysics
Tianya Luo, Longwei Chen, Xiangyun Hu
Summary: The wide application of conventional integral equation (IE) method in large-scale magnetotelluric (MT) modeling is restricted due to computational cost. This study proposes an improved approach to solve the storage and computational efficiency issues by using an analytical formula to calculate the Bessel function integral, leading to reduced memory and time consumption.
Article
Geochemistry & Geophysics
Hua Deng, Xiangyun Hu, Hongzhu Cai, Shuang Liu, Ronghua Peng, Yajun Liu, Bo Han
Summary: In this paper, a 3D magnetic gradient tensor (MGT) inversion method is developed using a convolutional neural network (CNN) for high-precision vector magnetic field detection. The CNN is used to automatically predict physical parameters from 2D images of MGT and a comprehensive 3D model is obtained. The reliability of the algorithm is verified through numerical simulations and field data validation, demonstrating the capability of using CNNs for inversing MGT data.
Article
Remote Sensing
Yue Xu, Jianya Gong, Xin Huang, Xiangyun Hu, Jiayi Li, Qiang Li, Min Peng
Summary: This paper constructs a large HSSR dataset based on aerial hyperspectral imagery and proposes a new deep learning model, 3D-HRNet, for interpreting HSSR images. The dataset is the largest of its kind to date and can be used for spatial-spectral feature extraction. The 3D-HRNet model shows good interpreting ability for the dataset, with a high FWIoU score of 80.54%.
GEO-SPATIAL INFORMATION SCIENCE
(2023)
Article
Geosciences, Multidisciplinary
Benteng Bi, Xiangyun Hu, Jingwen Li, Shan Xu
Summary: SE China is an important mineral resource base with distinct patterns of magmatism and mineralization due to uneven crust-mantle interactions.
GEOLOGICAL JOURNAL
(2023)
Review
Chemistry, Analytical
Wule Lin, Bo Yang, Bo Han, Xiangyun Hu
Summary: After 70 years of development, magnetotelluric (MT) has become a widely used remote sensing technique in resource exploration and the deep tectonic evolution of the Earth. This technique is based on the imaging of subsurface electrical resistivity anomalies, which are closely related to high-conductivity phases associated with tectonic activity. The review focuses on electrical conduction mechanisms, conductivity mixing models, potential causes of high-conductivity, and methods to infer water content in the upper mantle using MT.
Article
Chemistry, Multidisciplinary
Shujin Cao, Yihuai Deng, Bo Yang, Guangyin Lu, Xiangyun Hu, Yajing Mao, Shuanggui Hu, Ziqiang Zhu
Summary: Conventional Euler deconvolution is commonly used for interpreting potential field data, but cannot effectively separate adjacent targets. To overcome this limitation, we introduced multivariate Kernel Density Derivative Estimation (KDDE) as an extension of Kernel Density Estimation, which successfully discriminates spurious solutions and locates adjacent geological sources.
APPLIED SCIENCES-BASEL
(2023)
Article
Energy & Fuels
Shan Xu, Chang Ni, Xiangyun Hu
Summary: This study tackles the issue of insufficient heat flow observations in Northern China by utilizing the Gradient Boosted Regression Tree (GBRT) prediction model. Through training the sample data and considering geological and geophysical information, a reliable GBRT prediction model was obtained. Based on this model, a new heat flow map with a resolution of 0.25 degrees x 0.25 degrees was proposed, providing a more detailed and reasonable depiction of the terrestrial heat flow distribution in the study area compared to interpolation results. High heat flow values are concentrated in the northeastern boundary of the Tibet Plateau, with scattered and small-scale high heat flow areas in the southeastern part of the North China Craton (NCC) adjacent to the Pacific Ocean. Low heat flow values are mainly found in the northern part of the Trans-North China Orogenic belt (TNCO) and the southernmost part of the NCC.
Article
Geochemistry & Geophysics
Yuanzhi Cheng, Cheng Wang, Wenwei Da, Yanlong Kong, Xiangyun Hu
Summary: Southern China has abundant low-medium temperature geothermal resources, but the lack of research on their properties hinders the utilization of geothermal energy. This study aims to investigate the mechanisms of the Changshou geothermal field, a representative structurally controlled convective geothermal system, by analyzing geophysical and hydrochemical data. The study identifies the geological structure, thermal reservoir, and fluid pathways of the geothermal system, providing insights into its potential as a source of energy for residential heating in winter.
Article
Environmental Sciences
Qingyu Wu, Qiusheng Li, Xiangyun Hu, Zhanwu Lu, Wenhui Li, Xiaoran Wang, Guangwen Wang
Summary: This study explores the use of noise-based techniques for urban structure imaging and applies the beamforming method to analyze dense array data. The study demonstrates the potential of the beamforming method for processing dense array data sets and provides a high-resolution 3D S-wave velocity model for studying the Quaternary structure of the study area. The model has implications for understanding groundwater resources, urban infrastructure, and underground spaces.
