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Torque work of origin-destination flows: Quantifying urban place centrality from a physical perspective

Computers, Environment and Urban systems 2025年 121卷

作者： Xiaorui Yan Tao Pei Ci Song Zidong Fang Xiaohan Liu Tianyu Liu Linfeng Jiang Ying Gao Guangdong Li Jie Huang Yaqin Sun State Key Laboratory of Resources and Environmental Information System Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing China College of Earth and Environmental Sciences Lanzhou University Lanzhou China Key Laboratory of Regional Sustainable Development Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China School of Environment and Spatial Informatics China University of Mining and Technology Xuzhou China

Quantifying urban place centrality, defined as its relative importance in serving its peripheral areas, provides insights into urban structures, optimizes resource allocation, and supports strategic urban planning. Centrality is shaped by three aspects: service volume, spatial reach, and directional diversity. However, existing measures often assess these factors separately and few integrate them simultaneously. Additionally, centrality analyses often overlook local perspectives and intra-day dynamics. To this end, we propose a novel origin-destination flow-based centrality measure, namely Total Torque Work (TTW), that integrates these three aspects into a single value, conceptualized as “Torque work of flow”, where flow volume, length, and direction correspond to the force magnitude, lever arm, and angular displacement. The effectiveness of the TTW is validated by simulation experiments. We apply this measure to analyze macro- and micro-centralities in Beijing, using taxi and shared bike flow data. Macro-centrality shows a monocentric structure, with higher values near railway stations, airports, and business and commercial centers. Micro-centrality is more polycentric, with subway stations exhibiting higher centrality. Time series clustering identifies three temporal patterns in both macro- and micro-centralities: two “daytime-dominant” patterns linked to multifunctional activities and commuting, and a “nighttime-dominant” pattern in residential areas. The study concludes with several implications for urban planning, emphasizing the importance of incorporating multi-spatiotemporal scales.

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Regulation effect of planted forests on land surface temperature in China

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Agricultural and Forest Meteorology 2025年 372卷

作者： Zhijiang Zhang Kai Yang Lunche Wang Kai Cheng Chao Chen Chao Ding School of Geography Science and Geomatics Engineering Suzhou University of Science and Technology Suzhou 215009 China SuZhou Key Laboratory of Spatial Information Intelligent Technology and Application Suzhou University of Science and Technology Suzhou 215009 China School of Geography and Information Engineering China University of Geosciences Wuhan 430074 China Institute of Remote Sensing and Geographic Information System School of Earth and Space Sciences Peking University Beijing 100871 China Institute of Ecology College of Urban and Environmental Sciences Peking University Beijing 100871 China Department of Geographic Science Faculty of Arts and Sciences Beijing Normal University Zhuhai 519087 China

Afforestation has been regarded as a potentially effective strategy for the mitigation of climate warming. China has the largest planted forest area worldwide benefited by a series of large-scale afforestation and reforestation project since the 1970s. The planted forests (PF) have been widely reported as an enduring carbon sink. However, the climatic regulation effect of PF on climate through biophysical process remains unclear. To this end, we utilized satellite observations to quantify the impact of the conversion of grasslands (GRA), croplands (CRO) and natural forests (NF) to PF on land surface temperature (LST) between 2003 and 2012 and illustrated the underlying biophysical mechanisms based on a revised Intrinsic Biophysical Mechanism (IBM) method. The results indicated that afforestation of PF caused negligible daily cooling effect (−0.005 ± 0.002 K) compared with NF while induced obvious daily cooling effect compared with GRA (−0.253 ± 0.004 K) and CRO (−0.162 ± 0.002 K). We also demonstrated that the revised IBM attribution method is applicable in interpreting the biophysical mechanism of PF on LST. Attribution analysis revealed that the surface roughness and Bowen ratio dominated the cooling effect as the surface became rougher and more evaporative cooling after converting NF, GRA, or CRO to PF. The contributions of albedo were less stark compared with other components and presented a weak warming effect. Furthermore, the atmospheric feedback was proved that have a pronounced impact on LST and should be included in the attribution of LST change. This study focuses on the potential of PF afforestation to provide local climate adaptation benefits, filling the gaps of previous studies involved to biophysical effect failure in distinguishing NF and PF, and provides new guidance for local climate impact evaluation when implementing forestry projects.

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Transform-Invariant Convolutional Neural Networks for Image Classification and Search

arXiv

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arXiv 2019年

作者： Shen, Xu Tian, Xinmei He, Anfeng Sun, Shaoyan Tao, Dacheng CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui230027 China Centre for Quantum Computation & Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

Convolutional neural networks (CNNs) have achieved state-of-the-art results on many visual recognition tasks. However, current CNN models still exhibit a poor ability to be invariant to spatial transformations of images. Intuitively, with sufficient layers and parameters, hierarchical combinations of convolution (matrix multiplication and nonlinear activation) and pooling operations should be able to learn a robust mapping from transformed input images to transform-invariant representations. In this paper, we propose randomly transforming (rotation, scale, and translation) feature maps of CNNs during the training stage. This prevents complex dependencies of specific rotation, scale, and translation levels of training images in CNN models. Rather, each convolutional kernel learns to detect a feature that is generally helpful for producing the transform-invariant answer given the combinatorially large variety of transform levels of its input feature maps. In this way, we do not require any extra training supervision or modification to the optimization process and training images. We show that random transformation provides significant improvements of CNNs on many benchmark tasks, including small-scale image recognition, large-scale image recognition, and image retrieval. The code is available at https: ACM ISBN 978-1-4503-2138-9. Copyright © 2019, The Authors. All rights reserved.

关键词： Image recognition

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A TOPSAR Calibration Method for processing system of GF3 Next Generation 6

A TOPSAR Calibration Method for Processing System of GF3 Nex...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Yin, Di Han, Bing Sun, Jili Chen, Aiping Zhao, Liangbo Yuan, Xinzhe Zhong, Lihua Hu, Yuxin University of Chinese Academy of Sciences Beijing100049 China Institute of Electronics Chinese Academy of Sciences Beijing100190 China Beijing Institute of Telemetry and Telecommunications Technology Beijing100094 China Chinese Academy of Space Technology Beijing100094 China National Satellite Ocean Application Service State Oceanic Administration Beijing100081 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Institute of Electronics Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781728129129

TOPSAR is an earth-imaging technique, which can provide wide swath coverage. The paper introduces a TOPSAR focusing and calibrating experiment based on the TOPSAR data acquired by Gaofen3(GF3). In this paper, we firstly derive the processor calibration factors under the demands of keeping signal energy invariant. After that, we fully analyze the impact of antenna electronic steering on TOPSAR products. Aimed to be applied to TOPSAR mode processing system of a SAR satellite, the next generation of GF3, calibration methods to processor and electronic steering was proposed in this paper. © 2019 IEEE.

关键词： Calibration

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Patch reordering: A novel way to achieve rotation and translation invariance in convolutional neural networks

arXiv

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arXiv 2019年

作者： Shen, Xu Tian, Xinmei Sun, Shaoyan Tao, Dacheng CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui230027 China Centre for Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney 81 Broadway Street UltimoNSW2007 Australia

Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance on many visual recognition tasks. However, the combination of convolution and pooling operations only shows invariance to small local location changes in meaningful objects in input. Sometimes, such networks are trained using data augmentation to encode this invariance into the parameters, which restricts the capacity of the model to learn the content of these objects. A more efficient use of the parameter budget is to encode rotation or translation invariance into the model architecture, which relieves the model from the need to learn them. To enable the model to focus on learning the content of objects other than their locations, we propose to conduct patch ranking of the feature maps before feeding them into the next layer. When patch ranking is combined with convolution and pooling operations, we obtain consistent representations despite the location of meaningful objects in input. We show that the patch ranking module improves the performance of the CNN on many benchmark tasks, including MNIST digit recognition, large-scale image recognition, and image retrieval. The code is available at https://***//jasonustc/caffe-multigpu/tree/TICNN. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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Warning of Rainfall-Induced Landslide in Bazhou District

Warning of Rainfall-Induced Landslide in Bazhou District

引用

IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Mujie Li Mingcang Zhu Yang Hec Zhanyong He Na Wang Zezhong Zheng Guoqing Zhou School of Resources and Environment University of Electronic Science and Technology of China Chengdu Sichuan PRC Chengdu Sichuan PRC Sichuan Research Institute for Eco-system Restoration & Geo-disaster Prevention Chengdu Sichuan PRC Guangxi Key Laboratory for Spatial Information and Geomatics Guilin University of Technology Guilin Guangxi PRC

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Landslide disasters have caused incalculable losses to human. In China, 90% occurrence of landslides are directly induced by rainfall or indirectly related to rainfall. Because of its geographical location and the climate that belongs to the subtropical monsoon humid climate, the proportion of rainfall-induced landslides accounts for more than 70% of the total geological disasters in Bazhou district. In this paper, based on the geographic information system (GIS) technology, combined with the historical landslide hazard data, we conducted a study on landslide probabilistic quantitative model composed of landslide susceptibility evaluation and the rainfall intensity-duration threshold model. The research results showed that the prediction accuracy of rainfall model is 81.82%. And the landslide hazard prediction was carried out for rainfall-induced landslides and potential landslides, with a prediction accuracy of 90.91%. The research results showed that the meteorological early warning model results were consistent with the actual inspection results.

关键词： Terrain factors Biological system modeling Data models Predictive models Support vector machines Neural networks Hazards

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Parameter prediction method of SAR target simulation based on convolutional neural networks 12

Parameter prediction method of SAR target simulation based o...

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12th European Conference on Synthetic Aperture Radar, EUSAR 2018

作者： Shengren, Niu Xiaolan, Qiu Lingxiao, Peng Bin, Lei Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute ofElectronics Chinese Academy of Sciences University of Chinese Academy of Sciences China Suzhou Institute Institute of Electronics Chinese Academy of Sciences China

ISBN: (纸本)9783800746361

SAR image simulation plays a useful role in SAR target interpretation and recognition. The current SAR target simulation methods require high precision of models and simulation parameters, and are only forward processes which lack the feedback adjustment of real image. In this paper, a method of predicting simulation parameters from real images is proposed, which is based on convolutional neural networks(CNN). The architecture and the loss function of the CNN are modified to obtain better performance of the parameter inversion. From the simulation results, the predicted parameters improve the similarity between the simulation images and the real images. © VDE VERLAG GMBH Â Berlin Â Offenbach.

关键词： Synthetic aperture radar

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Bell State Preparation Based on Switching Between Quantum system Models

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Journal of systems Science & Complexity 2017年第2期30卷 347-356页

作者： ZHOU Juan KUANG Sen CONG Shuang Department of Automation University of Science and Technology of ChinaHefei 230027China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science

For the preparation of any target Bell state under continuous quantum measurement, this paper proposes a method which achieves the control objective by switching between two different models or by switching between two control channels under one model. Proper control Hamiltonians are selected for the two system models, a switching strategy between the two models is designed, and the stability of the whole switching system is proved in theory. For a given target Bell state, the effectiveness of the proposed switching control strategy between different models is illustrated through simulation experiments.

关键词： Bell state model switching quantum systems switching strategy.

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Real-time correlation tracking via joint model compression and transfer

arXiv

引用

arXiv 2019年

作者： Wang, Ning Zhou, Wengang Song, Yibing Ma, Chao Li, Houqiang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei China Tencent AI Lab Shenzhen China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

Correlation filters (CF) have received considerable attention in visual tracking because of their computational efficiency. Leveraging deep features via off-the-shelf CNN models (e.g., VGG), CF trackers achieve state-of-the-art performance while consuming a large number of computing resources. This limits deep CF trackers to be deployed to many mobile platforms on which only a single-core CPU is available. In this paper, we propose to jointly compress and transfer off-the-shelf CNN models within a knowledge distillation framework. We formulate a CNN model pretrained from the image classification task as a teacher network, and distill this teacher network into a lightweight student network as the feature extractor to speed up CF trackers. In the distillation process, we propose a fidelity loss to enable the student network to maintain the representation capability of the teacher network. Meanwhile, we design a tracking loss to adapt the objective of the student network from object recognition to visual tracking. The distillation process is performed offline on multiple layers and adaptively updates the student network using a background-aware online learning scheme. Extensive experiments on five challenging datasets demonstrate that the lightweight student network accelerates the speed of state-of-the-art deep CF trackers to real-time on a single-core CPU while maintaining almost the same tracking accuracy. Copyright © 2019, The Authors. All rights reserved.

关键词： Object recognition

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Self-Tuning Asynchronous Filter for Linear Gaussian system and applications

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IEEE/CAA Journal of Automatica Sinica 2018年第6期5卷 1054-1061页

作者： Wenjun Lv Yu Kang Yunbo Zhao Department of Automation University of Science and Technology of ChinaHefei 230027China State Key Laboratory of Fire Science Department of Automation and Institute of Advanced TechnologyUniversity of Science and Technology of ChinaHefei 230027China Key Laboratory of Technology in GeoSpatial Information Processing and Application System Chinese Academy of SciencesBeijing 100190China College of Information Engineering Zhejiang University of TechnologyHangzhou 310023China

In this paper, optimal filtering problem for a class of linear Gaussian systems is studied. The system states are updated at a fast uniform sampling rate and the measurements are sampled at a slow uniform sampling rate. The updating rate of system states is several times the sampling rate of measurements and the multiple is constant. To solve the problem,we will propose a self-tuning asynchronous filter whose contributions are twofold. First, the optimal filter at the sampling times when the measurements are available is derived in the linear minimum variance sense. Furthermore, considering the variation of noise statistics, a regulator is introduced to adjust the filtering coefficients adaptively. The case studies of wheeled robot navigation system and air quality evaluation system will show the effectiveness and practicability in engineering.

关键词： Air quality evaluation system linear Gaussian system wheeled robot navigation system

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