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2023 3rd International Conference on Computational Modeling, Simulation and Data Analysis, CMSDA 2023

作者： Tao, Sulin Li, Yuhong School of Applied Meteorology Nanjing University of Information Science and Technology Nanjing210044 China Technology Development Centre Nanjing Daqiao Machine Co. Ltd. Nanjing211101 China Liaoning Ecological Meteorology and Satellite Remote Sensing Centre Shenyang110166 China Institute of Atmospheric Environment China Meteorological Administration Shenyang110166 China

A scaling region identification method for divider dimensions using the coefficient of determination is proposed to improve the estimation accuracy of the divider dimension in the study of urban thermal environment differentiation characteristics. The effectiveness of this method was verified by analyzing the urban surface thermal field differentiation characteristics by applying the estimated divider dimensions of Nanjing's thermal environment surfaces and transect lines. The research results show that the urban thermal environment has fractal characteristics only within a certain temperature threshold range. When the constraint of scaling regions is considered, the calculated divider dimension of the surface thermal field surface increases, reflecting the more complex fractal structure of the thermal field. For each temperature grade, the "trough"position of the thermal field divider dimension surface estimated by the line-divider method is near the determination level of the high-Temperature region based on robust statistics, allowing the spatial extent of the urban heat island (UHI) to be determined based on the "trough"feature of the thermal field divider dimension surface. © Published under licence by IOP Publishing Ltd.

关键词： Fractal dimension

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RailPC: A large-scale railway point cloud semantic segmentation dataset

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1548-1560页

作者： Tengping Jiang Shiwei Li Qinyu Zhang Guangshuai Wang Zequn Zhang Fankun Zeng Peng An Xin Jin Shan Liu Yongjun Wang Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing Normal UniversityNanjingChina Technology Innovation Center for Integrated Applications in Remote Sensing and Navigation Ministry of Natural ResourcesNanjingChina Eastern Institute of Technology(EIT) NingboChina Tianjin Key Laboratory of Rail Transit Navigation Positioning and Spatio-temporal Big Data Technology TianjinChina College of Computer Science and Engineering Northwest Normal UniversityLanzhouChina Mckelvey School of Engineering Washington University in St.LouisSt.LouisMissouriUSA School of Electronic and Information Engineering Ningbo University of TechnologyNingboChina

Semantic segmentation in the context of 3D point clouds for the railway environment holds a significant economic value,but its development is severely hindered by the lack of suitable and specific ***,the models trained on existing urban road point cloud datasets demonstrate poor generalisation on railway data due to a large domain gap caused by non-overlapping special/rare categories,for example,rail track,track bed *** harness the potential of supervised learning methods in the domain of 3D railway semantic segmentation,we introduce RailPC,a new point cloud *** provides a large-scale dataset with rich annotations for semantic segmentation in the railway ***,RailPC contains twice the number of annotated points compared to the largest available mobile laser scanning(MLS)point cloud dataset and is the first railway-specific 3D dataset for semantic *** covers a total of nearly 25 km railway in two different scenes(urban and mountain),with 3 billion points that are finely labelled as 16 most typical classes with respect to railway,and the data acquisition process is completed in China by MLS *** extensive experimentation,we evaluate the performance of advanced scene understanding methods on the annotated dataset and present a synthetic analysis of semantic segmentation *** on our findings,we establish some critical challenges towards railway-scale point cloud semantic *** dataset is available at https://***/NNU-GISA/GISA-RailPC,and we will continuously update it based on community feedback.

关键词： data benchmark MLS point clouds railway scene semantic segmentation

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The spectral conjugate gradient method in variational adjoint assimilation for model terrain correction III: Convergence analysis 3

The spectral conjugate gradient method in variational adjoin...

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2023 3rd International Conference on Computational Modeling, Simulation and Data Analysis, CMSDA 2023

作者： Tao, Sulin Li, Yuhong School of Applied Meteorology Nanjing University of Information Science and Technology Nanjing210044 China Technology Development Center Nanjing Daqiao Machine Co. Ltd. Nanjing211101 China Liaoning Ecological Meteorology and Satellite Remote Sensing Center Shenyang110166 China Institute of Atmospheric Environment China Meteorological Administration Shenyang110166 China

In the former parts of this paper, the spectral conjugate gradient (SCG) algorithm was proposed to identify the optimal bottom terrain used for predicting the meteorological fields. In this part, the convergence analysis of the SCG method is presented to verify its validity when solving a typical ill-posed inverse problem. The scaled cost function, the scaled gradient norm, the linear convergence rate, and the Hessian approximation condition number are evaluated for convergence analysis. The SCG method reduces the cost functional value and the gradient norm sufficiently. It always performs linear rates of convergence with a quickly reduced condition number of the Hessian matrix. Sometimes it provides very fast linear convergence rates. The SCG code with optimal step size and Andrei's equation is effective on ill-posed test problems. © Published under licence by IOP Publishing Ltd.

关键词： Cost functions

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Three-dimensional gravity inversion based on optimization processing from edge detection

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Geodesy and Geodynamics 2022年第5期13卷 503-524页

作者： Sheng Liu Shuanggen Jin Qiang Chen Xinjiang Astronomical Observatory Chinese Academy of SciencesUrumqi 830011China University of Chinese Academy of Sciences Beijing 100049China Shanghai Astronomical Observatory Chinese Academy of SciencesShanghai 200030China School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and TechnologyNanjing 210044China

Gravity inversion requires much computation,and inversion results are often *** first problem is often due to the large number of grid *** detection method,i.e.,tilt angle method of analytical signal amplitude(TAS),helps to identify the boundaries of underground geological anomalies at different depths,which can be used to optimize the grid and reduce the number of grid *** requirement of smooth inversion is that the boundaries of the meshing area should be continuous rather than *** this paper,the optimized meshing strategy is improved,and the optimized meshing region obtained by the TAS is changed to a regular region to facilitate the smooth *** the second problem,certain constraints can be used to improve the accuracy of *** results of analytic signal amplitude(ASA)are used to delineate the central distribution of geological *** propose a new method using the results of ASA to perform local constraints to reduce the non-uniqueness of *** guided fuzzy c-means(FCM)clustering algorithm combined with priori petrophysical information is also used to reduce the non-uniqueness of gravity *** Open Acc technology is carried out to speed up the computation for parallelizing the serial program on *** general,the TAS is used to reduce the number of grid *** local weighting and priori petrophysical constraint are used in conjunction with the FCM algorithm during the inversion,which improves the accuracy of *** inversion is accelerated by the Open Acc technology on *** proposed method is validated using synthetic data,and the results show that the efficiency and accuracy of gravity inversion are greatly improved by using the proposed method.

关键词： Gravity inversion Locally weighted constraint Petrophysical constrain Fuzzy c-means clustering algorithm Open Acc technology

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Wavefront-Coded Image Restoration technology Based on Multi-Scale Deep Neural Networks

SSRN

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SSRN 2024年

作者： Zhen, Zheng Yuan, Yiqiao Zhang, Bo Liang, Zonglin Li, Chenxiao Piao, Mingxu Dong, Keyan Jiang, Huilin School of Optoelectronic Engineering Changchun University of Science and Technology Changchun130022 China Institute of Space Optoelectronic Technology Changchun University of Science and Technology Changchun130022 China Beijing Institute of Remote Sensing Equipment Beijing100854 China

Optical imaging systems are significantly affected by aerodynamic thermal effects under varying flight conditions, resulting in complex image blurring. To address this challenge, this study proposes a novel wavefront-coded image restoration method based on a Multi-Scale Deep Autoencoder Neural Network (MS-DAE). By modulating blur levels and incorporating a multi-scale loss function with residual attention mechanisms, the proposed method achieves a remarkable improvement in Peak Signal-to-Noise Ratio (PSNR) by 15.52 dB and Structural Similarity Index Measure (SSIM) by 0.3867 compared to Wiener filtering. The approach effectively restores image details, suppresses artifacts, and adapts to diverse flight conditions, demonstrating significant potential for practical applications. © 2024, The Authors. All rights reserved.

关键词： Optical transfer function

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Analysis of the common model error on velocity field under Colored noise model by GPS and InSAR: A case study in the Nepal and everest region

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Geodesy and Geodynamics 2022年第4期13卷 399-414页

作者： Wei Xu Gang Chen Kaihua Ding Defang Yang Yanfa Si Xiaoying Yang Qinghai Remote Sensing Center for Natural Resources Xining 810000China College of Marine Science and Technology China University of GeosciencesWuhan 430074China School of Geography and Information Engineering China University of GeosciencesWuhan 430074China Institute of Geophysics and Geomatics China University of GeosciencesWuhan 430074China

The accuracy of the velocity field will be affected by the noise model and common mode errors through GPS time series *** order to analyze the influence of these two factors on the accuracy of the velocity field,two kinds of data are used,including the three-year observation from 20 permanent GPS stations with high spatial correlation in the Everest,which is about 650 km from north to south and 1068 km from east to west,and three-year 80 ascending images and 141 descending images from sentinel-1A,which are processed by GAMIT/GLOBK software and Small Baseline Subset-Interferometric Synthetic Aperture Radar method(SBAS-InSAR),*** vertical deformation rate is solved by time series analysis through a self-made adaptive *** the analysis,the linear change rate,period,half period coefficient,and residual sequence of all stations are solved by using James *** periodic *** noise type of residual sequence is analyzed by the power spectrum *** spatio-temporal correlated noise,Common Mode Error(CME),is extracted by the Principal Component Analysis(PCA)and Karhunen-Loeve(KLE)*** results show that noises can be best described by“flicker noise+white noise”*** the removal of CME,the R^(2) estimates of all stations are above 0.8,with RMS value of velocity field decreasing from 1.428 mm/yr to 1.062 mm/yr and 1.063 mm/yr to 0.815 mm/yr,in N and E directions,respectively,indicating that the influence of CME can't be ignored in the extraction of the high-precision velocity field in the Nepal and Everest region.

关键词： GPS time analysis SBAS-InSAR Power spectrum analysis PCA KLE CME

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Reconstruction of Spatially Seamless Soil Moisture Data from SMAP Product Using Deep Learning

Reconstruction of Spatially Seamless Soil Moisture Data from...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： H. Y. Fan Z. S. Li Y. Jin Y. Jia S. G. Jin School of Internet of Things Nanjing University of Posts and Telecommunications China School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and Technology China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

This paper proposes a deep learning method for spatial data imputation, utilizing a CNN with residual connections to construct a regression model for missing value prediction. The introduction of temporal features from SMAP ensures seamless spatial characteristics for soil moisture. By integrating various spatially seamless, multi-source remote sensing data as auxiliary variables, the method predicts and fills the spatial gaps in SMAP SSM. The result is a long-term, spatially seamless daily soil moisture dataset with a 9km resolution, covering the period from January 1, 2016, to December 31, 2022. The effectiveness of this spatial imputation method is validated by comparing and analyzing it with site-measured data and original SMAP data. This approach provides a solution for generating spatially seamless soil moisture data.

关键词： Deep learning Soil moisture Predictive models Data models Spatial databases Imputation Spatial resolution

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Research on Cloud Detection Method of GaoFen-6 Wide Camera Data 7th

Research on Cloud Detection Method of GaoFen-6 Wide Camera D...

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7th China High-resolution Earth Observation Conference, CHREOC 2020

作者： Ke, Shiyun Wang, Mi Cao, Jinshan Long, En State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China School of Computer Science Hubei University of Technology 28 Nanli Road Wuhan430068 China Beijing Institute of Remote Sensing Information 2 Xiaoyingdong Road Beijing100192 China

ISBN: (纸本)9789811657344

Cloud detection is one of the research hotpots in high-resolution remote sensing images, and is an important part of remote sensing image preprocessing. The traditional physical threshold method based on pixel is inefficient when the amount of data is large. It is also easily affected by the noise points in the image, and the appropriate spectral threshold is difficult to determine. In view of the above problems, this paper starts from the GF-6 wide camera data, studies the spectral characteristics of GF-6 wide camera data, and selects the appropriate spectral detection band. Then, in order to improve the universality, using the maximum inter class variance method (OTSU) to adaptively obtain the appropriate cloud detection threshold. Finally, the image is segmented by simple linear iterative clustering algorithm (SLIC). Super pixel is used as the processing unit of subsequent image processing to replace single pixel for cloud detection. It both improves validity and accuracy. In this paper, by doing experiments and quantitative evaluation, it can be proved that the cloud detection algorithm can effectively and accurately detect the cloud region in the image. The accuracy was all higher than 90%, and the average accuracy of the experiment is 94.21%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cameras

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Regional Tropospheric Delay Prediction Model Based on LSTM-Enhanced Encoder Network

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IEEE Journal of Selected Topics in Applied Earth Observations and remote sensing 2025年 18卷 13348-13358页

作者： Peng, Yuanfang Cai, Chenglin Li, Zexian Lv, Kaihui Zhang, Xue Cai, Yihao Xiangtan University Intelligent Navigation and Remote Sensing Research Center School of Automation and Electronic Information Xiangtan411105 China Xiangtan University National Mathematics Application Center School of Automation and Electronic Information Xiangtan411105 China Xiangtan University Intelligent Navigation and Remote Sensing Research Center School of Mathematics and Computational Science Xiangtan411105 China ShanghaiTech University School of Physical Science and Technology Shanghai200031 China

Precise modeling of Zenith Tropospheric Delay (ZTD) is essential for real-time high-precision positioning in global navigation satellite systems (GNSS). Due to the stochastic variability of atmospheric water vapor across different regions, tropospheric delay exhibits strong regional characteristics. Empirical tropospheric delay models built on reanalysis of meteorological data often show significant accuracy discrepancies across regions, failing to meet the needs for precise regional ZTD forecasting. Deep learning methods excel in learning complex patterns and dependencies from time series data. Our study utilized ZTD data from 178 NGL stations in Australia during 2023 as ground truth values and modeled them using an LSTM-Enhanced encoder network. This model incorporated both spatial and temporal information as well as correlations with GPT3 ZTD. Predictions were compared with those from GPT3 ZTD, ERA5 ZTD, ANN ZTD, GRNN ZTD and LSTM ZTD. The results showed that the LSTM-Enhanced encoder ZTD achieved an RMSE of 14.43 mm, a mean bias close to zero, with mean absolute error and mean correlation coefficient of 12.42 mm and 0.95, respectively. The proposed model outperforms the GPT3, ERA5, ANN, GRNN, and LSTM models, with respective RMSE improvements of approximately 62.3%, 12.3%, 61%, 59.9%, and 60%. In addition, we compared the spatial and temporal properties of the proposed model with those of the GPT3 model and the ERA5 model. The discussion section further analyzed the prediction performance of different neural network approaches under different prediction periods. © 2008-2012 IEEE.

关键词： Troposphere

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ULIN-D: Web-based GIS supporting new habits in the tourism sector in Bandung city

ULIN-D: Web-based GIS supporting new habits in the tourism s...

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American Society for Photogrammetry and remote sensing, ASPRS 2021 Annual Conference

作者： Nur Ihsan, Kalingga Titon Purnomo, Andhika Dimas Arini, Kirana Sekar Remote Sensing and Geographic Information Science Research Group Faculty of Earth Sciences and Technology Institut Teknologi Bandung Indonesia Center for Remote Sensing Institut Teknologi Bandung Indonesia

Indonesia is one of the countries that has enormous tourism potential where this sector has contributed a total of 229.5 trillion for the country's foreign exchange. However, in the current state of the COVID-19 pandemic, many businesses in the tourism sector have to stop temporarily and result in economic pressure which directly impacts the acceleration of the economic recession in Indonesia. One of the solutions to reduce the impact of economic pressure while reducing the risk of COVID-19 transmission in the tourism sector is the application of health protocols that are directly applied in tourist attractions. To do this a variety of careful preparations are needed. In this case, geospatial information technology can help the community, government, and tourism business managers in implementing health protocols in tourist attractions. Seeing the urgency of implementing health protocols in the tourism sector, we developed the ULIN-D product as a WebGIS platform that is able to visualize, plan, and evaluate the condition of health protocol facilities in various tourist locations. Some of ULIN-D's strategic functions are its ability to provide recommended visitor capacity, display the number of visitors in real-time, and display the number of hand-washing places at tourist attractions based on the profile characteristics of each tourist location. The method used in this research is the combination of various kinds of spatial data and non-spatial data in a dynamic and integrated platform. Tourism location profile information is analyzed in detail to obtain accurate scenario results in establishing a standard health protocol. ULIN-D is expected to be a solution and a reference for how geospatial technology can help improve the quality of policies in the tourism sector for the continuation of the new habits adaption. © 2021 International Society for Photogrammetry and remote sensing. All rights reserved.

关键词： Location

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