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Spatial Pattern of Housing Sales Vacancy in Guangzhou’s Urban District, China

Journal of World Architecture 2021年第6期5卷 47-51页

作者： Xiaoli Yue Yang Wang Yabo Zhao Hong’ou Zhang Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and ApplicationGuangzhou Institute of GeographyGuangdong Academy of SciencesGuangzhou 510070Guangdong ProvinceChina School of Architecture and Urban Planning Guangdong University of TechnologyGuangzhou 510090Guangdong ProvinceChina Faculty of Geography Yunnan Normal UniversityKunming 650500Yunnan ProvinceChina

Housing vacancy can reflect the destocking degree of the real estate *** on the data of 57 opened residential quarters(46,622 units)from 2015 to 2018,this paper constructs a calculation formula of the sales vacancy rate and then analyzes the spatial pattern in Guangzhou’s urban *** results show that there is obvious differentiation in the spatial pattern of housing sales vacancy in Guangzhou’s urban district,showing a higher spatial pattern in the old area and urban district and a lower spatial pattern in the core *** with high vacancy rates are mainly located in the east of the old area,the south and east of the urban district and near Baiyun Mountain in the north.

关键词： Housing vacancy Sales vacancy Guangzhou’s urban district Spatial pattern

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China’s 10-year progress in DC gas-insulated equipment:From basic research to industry perspective

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iEnergy 2022年第4期1卷 400-433页

作者： Chuanyang Li Changhong Zhang Jinzhuang Lv Fangwei Liang Zuodong Liang Xianhao Fan Uwe Riechert Zhen Li Peng Liu Jianyi Xue Cheng Pan Geng Chen Lei Zhang Zheming Wang Wu Lu Hucheng Liang Zijun Pan Weijian Zhuang Giovanni Mazzanti Davide Fabiani Bo Liu Shaohua Cao Jianying Zhong Yuan Deng Zhenle Nan Jingen Tang Jinliang He State Key Laboratory of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Maintenance&Test Center of EHV Power Transmission Company China Southern Power Grid Guangzhou 510663China EHV Power Transmission Company China Southern Power Grid Guangzhou 510663China Hitachi Energy Switzerland Ltd ZürichSwitzerland Key Laboratory of Engineering Dielectrics and Its Application Ministry of EducationHarbin University of Science and TechnologyHarbin 150080China State Key Laboratory of Electrical Insulation and Power Equipment Xi’an Jiaotong UniversityXi’an 710049China School of Electrical Engineering and Automa-tion Hefei University of TechnologyHefei 230009China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China Department of Electrical Engineering Shanghai University of Electric PowerShanghai 200090China School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China Department of Electrical Electronic and Information EngineeringUniversity of BolognaBologna 40136Italy Shandong Taikai Electrical Insulation Co. Ltd.Tai’an 271000China PingGao Group CO. Ltd.Pingdingshan 467001China China XD Group Co. Ltd.Xi’an 710075China Jiangsu Jinxin Electric Appliance Co. Ltd.Yangzhou 225202China

The construction of the future energy structure of China under the 2050 carbon-neutral vision requires compact direct current(DC)gas-insulation equipment as important nodes and solutions to support electric power transmission and distribution of long-distance and *** paper reviews China's 10-year progress in DC gas-insulated *** progresses in basic research and industry perspective are presented,with related scientific issues and technical bottlenecks being *** progress in DC gas-insulated equipment worldwide(Europe,Japan,America)is also reported briefly.

关键词： DC gas-insulated switchgear(GIS) DC gas-insulated power transmission line(GIL) surface charge flashover metal particle offshore projects

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Corrigendum to "Simultaneous realization of giant d33, large kp, and high Qm from periodic interface strain generated by co-doping strategy" [Acta Materialia 288 (2025) 120770] (Acta Materialia (2025) 288, (S1359645425000631), (10.1016/***.2025.120770))

引用

Acta Materialia 2025年 293卷

作者： Huang, Xiaolin Xing, Bohan Wang, Yu Liu, Mingxuan Wen, Xing Jin, Xinyu Li, Danyang Tan, Peng Tian, Hao School of Physics Harbin Institute of Technology Harbin150001 China Laboratory for Space Environment and Physical Sciences Harbin Institute of Technology Harbin150001 China Heilongjiang Provincial Key Laboratory of Plasma Physics and Application Technology Harbin150001 China Key Laboratory of Micro-Nano Optoelectronic Information System Ministry of Industry and Information Technology Harbin150001 China

The authors regret that in Figs. 1 (a, c), the XPS result of pristine KTN and the Εr versus temperature of Fe-doped KTN were misused. The corrected figures are shown as follows. The corrections do not have any impact on the core content and conclusions of the paper. In Fig. 9(c1), the HRTEM image of Fe-doped KTN was misused and did not match the original results of Figs. 9(c2-c4). The HRTEM image of Fe-doped KTN is corrected as follows. Because the conclusions are mainly based on the results of Figs. 9(c2-c4), which are correct in the original version, this correction does not have any impact on the core content and conclusions of the paper. The authors would like to apologise for any inconvenience caused. © 2025 Acta Materialia Inc.

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A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective

arXiv

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arXiv 2021年

作者： Li, Xin Jin, Xin Fu, Jun Yu, Xiaoyuan Tong, Bei Chen, Zhibo The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China The Eastern Institute for Advanced Study China Huawei Cloud China

Collecting amounts of distorted/clean image pairs in the real world is non-trivial, which seriously limits the practical applications of these supervised learning-based methods on real-world image super-resolution (RealSR). Previous works usually address this problem by leveraging unsupervised learning-based technologies to alleviate the dependency on paired training samples. However, these methods typically suffer from unsatisfactory texture synthesis due to the lack of supervision of clean images. To overcome this problem, we are the first to have a close look at the under-explored direction for RealSR, i.e., few-shot real-world image super-resolution, which aims to tackle the challenging RealSR problem with few-shot distorted/clean image pairs. Under this brand-new scenario, we propose Distortion Relation guided Transfer Learning (DRTL) for the few-shot RealSR by transferring the rich restoration knowledge from auxiliary distortions (i.e., synthetic distortions) to the target RealSR under the guidance of distortion relation. Concretely, DRTL builds a knowledge graph to capture the distortion relation between auxiliary distortions and target distortion (i.e., real distortions in RealSR). Based on the distortion relation, DRTL adopts a gradient reweighting strategy to guide the knowledge transfer process between auxiliary distortions and target distortions. In this way, DRTL could quickly learn the most relevant knowledge from the synthetic distortions for the target distortion. We instantiate DRTL with two commonly-used transfer learning paradigms, including pre-training and meta-learning pipelines, to realize a distortion relation-aware Few-shot RealSR. Extensive experiments on multiple benchmarks and thorough ablation studies demonstrate the effectiveness of our DRTL. Copyright © 2021, The Authors. All rights reserved.

关键词： Textures

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UAVs as remote sensing platforms in plant ecology:review of applications and challenges

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Journal of Plant Ecology 2021年第6期14卷 1003-1023页

作者： Zhongyu Sun Xiaonian Wang Zhihui Wang Long Yang Yichun Xie Yuhui Huang Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of GeographyGuangdong Academy of SciencesGuangzhou 510070China School of Geographical Sciences South China Normal UniversityGuangzhou 510631China Department of Geography and Geology Eastern Michigan UniversityYpsilantiMI 48197USA Guangdong Provincial Key Laboratory of Silviculture Protection and UtilizationGuangdong Academy of ForestryGuangzhou 510520China

Aims Unmanned aerial vehicles(UAVs),***,have recently emerged as cost-effective and flexible tools for acquiring remote sensing data with fine spatial and temporal *** provides a new method and opportunity for plant ecologists to study issues from individual to regional ***,as a new method,UAVs remote sensing applications in plant ecology are still *** needs of plant ecology research and the application development of UAVs remote sensing should be better *** This report provides a comprehensive review of UAV-based remote sensing applications in plant ecology to synthesize prospects of applying drones to advance plant ecology *** Findings Of the 400 references,59%were published in remote sensing journals rather than in plant ecology journals,reflecting a substantial gap between the interests of remote sensing experts and plant *** of the studies focused on UAV remote sensing’s technical aspects,such as data processing and remote sensing inversion,with little attention on answering ecological *** were 61%of studies involved community-scale *** and multispectral cameras were the most used sensors(75%).More ecologically meaningful parameters can be extracted from UAV data to better understand the canopy surface irregularity and community heterogeneity,identify geometrical characteristics of canopy gaps and construct canopy chemical assemblies from living vegetation *** cooperation between plant ecologists and remote sensing experts is needed to promote UAV remote sensing in advancing plant ecology research.

关键词： UAVs drones unmanned aircraft systems(UASs) plant ecology species identification community function

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A Multiple Moving Ships Detection Method for GF-4 Satellite Image in Thin-Cloud Environment 1

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6th China High Resolution Earth Observation Conference, CHREOC 2019

作者： Lv, Peng Hu, Yuxin Li, Qianqian Hou, Yangshuan Wang, Xiaohui Lei, Bin Institute of Electronics Chinese Academy of Sciences Beijing China Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9789811539473

ISBN: (纸本)9789811539466

At present, the GF-4 satellite is the world’s highest resolution geostationary orbit optical imaging satellite. The GF-4 satellite has the advantages of wide-swath and high-frequency imaging, so it can provide quasi-real-time images for ship surveillance. In fact, using the GF-4 satellite image to detect moving ships is to detect the gray value of the wake formed by the ship moving at sea. However, because of the geometric features of the ship’s wake vary with the speed, the ship detection method based on geometric features is not applicable. Since, not only the ship is moving in the image but also the cloud is moving all the time, the optical flow method is not applicable. Ship detection methods based on machine learning and deep learning are also not applicable due to too few texture features of the wake. In this paper, we present a novel method for ship detection in thin-cloud environments. The real image processing of GF-4 satellite shows that the proposed method can detect multiple moving ships accurately and quickly in a thin-cloud environment. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Ships

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Research and application of Automatic Classification Method for Patrol Targets of Transmission Lines Based on LiDAR Point Cloud 6th

Research and Application of Automatic Classification Method ...

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6th International Conference on Geo-informatics in Sustainable Ecosystem and Society, GSES 2018

作者： Zhang, Chen Li, Yong Zhou, Xia Yang, Ji Yang, Chuanxun Liu, Hongkai Guangzhou Institute of Geography Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou510070 China

ISBN: (纸本)9789811370243

The development of airborne laser radar technology and cloud data processing technology provides a new technical means for the acquisition and processing of geospatial 3D data. Based on the self-developed LiDAR UAV, this paper proposes a new point cloud automatic classification method (TL-PCACM) of transmission lines by using the improved fast 3D convex hull construction algorithm, k-means clustering analysis algorithm and Region-growing algorithm, which have high classification accuracy, good classification effect and fast processing speed. The accuracy of point cloud automatic classification is over 95%, the efficiency can reach 60 km/h. The proposed method is applied to the analysis of tree obstacles under transmission lines. The efficiency of the automatic identification of tree obstacles can reach 720 towers/hour. The accuracy can reach 50 cm. It provides high efficiency, high quality, fully automatic and intelligent data processing method for the classification of tree obstacles, effectively improving the processing accuracy and speed of geospatial 3D information data. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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THE REMOTE SENSING IMAGE GEOMETRICAL MODEL of BP NEURAL NETWORK

THE REMOTE SENSING IMAGE GEOMETRICAL MODEL of BP NEURAL NETW...

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2020 International Conference on Geomatics in the Big Data Era, ICGBD 2020

作者： Yue, C.Y. Sun, T. Xie, J.F. Beijing Institute of Space Mechanics and Electricity Beijing China Beijing Key Laboratory of Advanced Optical Remote Sensing Technology Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Land Satellite Remote Sensing Application Center Ministry of Natural Resources of P. R. China Beijing China

Imagery geometry models (IGMs) of the high-resolution satellite images (HRSIs) are always of great interest in the photogrammetry and remote sensing community for the raising new kinds of sensors and imaging systems. Especially the generalized sensor models (GSMs) have been widely used for positioning of satellite images, and the accuracy are already validated. Since Back propagation (BP) neural network is a better choice for the two key reasons of the replacement of physical sensor models by generalized sensor models, numerous mathematical estimations for every specialized sensor, and secret equations of the IGMs. Experiments are carried out to test the approximation accuracy of the new generalized sensor model. And the experimental results show that, the BP neural network is of extremely high accuracy for satellite imagery photogrammetric restitution. © 2020 C. Y. Yue et al.

关键词： Backpropagation

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A Robust and Efficient Boundary Point Detection Method by Measuring Local Direction Dispersion

arXiv

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arXiv 2023年

作者： Peng, Dehua Gui, Zhipeng Gui, Jie Wu, Huayi School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China Collaborative Innovation Center of Geospatial Technology Wuhan University Wuhan430079 China School of Cyber Science and Engineering Southeast University Nanjing211189 China Engineering Research Center of Blockchain Application Supervision and Management Southeast University Nanjing211189 China Purple Mountain Laboratories Nanjing210000 China

Boundary point detection aims to outline the external contour structure of clusters and enhance the inter-cluster discrimination, thus bolstering the performance of the downstream classification and clustering tasks. However, existing boundary point detectors are sensitive to density heterogeneity or cannot identify boundary points in concave structures and high-dimensional manifolds. In this work, we propose a robust and efficient boundary point detection method based on Local Direction Dispersion (LoDD). The core of boundary point detection lies in measuring the difference between boundary points and internal points. It is a common observation that an internal point is surrounded by its neighbors in all directions, while the neighbors of a boundary point tend to be distributed only in a certain directional range. By considering this observation, we adopt density-independent K-Nearest Neighbors (KNN) method to determine neighboring points and design a centrality metric LoDD using the eigenvalues of the covariance matrix to depict the distribution uniformity of KNN. We also develop a grid-structure assumption of data distribution to determine the parameters adaptively. The effectiveness of LoDD is demonstrated on synthetic datasets, real-world benchmarks, and application of training set split for deep learning model and hole detection on point cloud data. The datasets and toolkit are available at: https://***/ZPGuiGroupWhu/lodd. Copyright © 2023, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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Large-Scale Single Tree information Extraction of Oil Palm in Malaysia Based on Sub-Meter Visible Light Satellite Images

SSRN

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SSRN 2024年

作者： Zhang, Sen Li, Nan Cui, Yaoping Dong, Jinwei Yu, Le Ran, Lishan Chen, Ziyue Niu, Jiqiang Yan, Wei Kanniah, Kasturi Devi Xiao, Xiangming Zhang, Yongguang Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions Henan University Ministry of Education Kaifeng475004 China College of Geography and Environmental Science Henan University Kaifeng475004 China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application International Institute for Earth System Sciences Nanjing University Nanjing210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing100084 China Department of Geography The University of Hong Kong Pokfulam999077 Hong Kong State Key Laboratory of Earth Surface Processes and Resource Ecology College of Global Change and Earth System Science Beijing Normal University Beijing100875 China College of Geographic Sciences Xinyang Normal University Xinyang464000 China Department of Geoinformation Faculty of Built Environment & Surveying Universiti Teknologi Malaysia Johor Bahru81310 Malaysia Department of Microbiology and Plant Biology Center for Spatial Analysis University of Oklahoma NormanOK73019 United States College of Environment and Planning Faculty of Geoinformation and Real Estate

The distribution of specific tree species and single tree information can shift down the research scale of geography and ecology, bridging research from regional to local scales. However, efficiently and finely extracting the spatial distribution and single tree information for a tree species regionally has always been a challenge due to the constraints of data and algorithms. With sub-meter visible light satellite images (RGB bands) in 2021 to 2022, we constructed a novel method for extracting the spatial distribution and single tree information of oil palm across Malaysia, namely the Single Tree Extraction Method - Oil Palm (STEM-OP). We developed a graphic morphological algorithm based on gradient difference window detection to extract the spatial distribution of oil palms. Then, we proposed an algorithm of double ring sliding detection algorithm to identify the canopy texture features of single oil palm. Finally, an adaptive iterative erosion algorithm was employed to separate overlapping and adjacent canopies into single trees. The overall accuracy of both the spatial distribution and number of single oil palm extracted by STEM-OP exceeded 90%. Our study revealed that the total area of oil palm in Malaysia was 5.25 million hectares, with a total of 572.6 million trees and an average planting density of 109 palms per hectare. The STEM-OP addresses the computational inefficiencies of methods with training samples and limitations of satellite image bands. For the first time, our study extracts the refined spatial distribution of oil palm and obtains the planting density and single tree position on a national scale. All of these are of great value to the formulation of oil palm development policies and ecological protection measures. © 2024, The Authors. All rights reserved.

关键词： Morphology

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