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Identifying Nighttime Vitality Through Multisource Geodata: An Explainable AI Perspective

Transactions in GIS 2025年第3期29卷

作者： Li, Sheng Liang, Xiaojin Yu, Jie Qiu, Tianqi Wu, Chao Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources Shenzhen China School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China Shenzhen Data Management Center of Planning and Natural Resource Shenzhen Geospatial Information Center Shenzhen China Guangdong Enterprise Key Laboratory for Urban Sensing Monitoring and Early Warning Guangzhou China Collaborative Innovation Center for Natural Resources Planning and Marine Technology of Guangzhou Guangzhou China Guangzhou Urban Planning & Design Survey Research Institute Co. Ltd. Guangzhou China Renmin Middle School of Nanjing Nanjing China Key Laboratory of Virtual Geographic Environment Ministry of Education Nanjing Normal University Nanjing China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing Normal University Nanjing China

Nighttime vitality serves as a crucial criterion for assessing the attractiveness and competitiveness of urban areas. The varying and multifaceted characteristics of nighttime vitality across neighborhoods reflect residents' preferences, social demands, and localized economies. The objective of this study is to comprehensively assess nighttime vitality by integrating multiple geospatial data sources, including mobile phone data, nighttime remote sensing data, and small catering businesses data. Furthermore, within an explainable artificial intelligence (XAI) framework, this study aims to quantitatively analyze the nonlinear relationships between the influencing factors and nighttime vitality. To enhance interpretability, we employ SHapley Additive exPlanations (SHAP) for visual model interpretation. This study can further enhance the theory of urban vitality, deepen the understanding of nonlinear relationships, and facilitate the implementation of explainable AI in urban studies. The findings of this study can effectively provide guidance for urban planning and decision-making, improve residents' quality of life, and promote sustainable urban development. © 2025 John Wiley & Sons Ltd.

关键词： Urban growth

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3D Point Cloud Reconstruction of Vehicle using Inversely Mapping and Voting from CSAR Images

3D Point Cloud Reconstruction of Vehicle using Inversely Map...

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IEEE International Conference on Radar

作者： Shanshan Feng Yun Lin Fei Teng Wen Hong Key Laboratory of Technology in Geospatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences University of Chinese Academy of Sciences Institute of Electronics Chinese Academy of Sciences Beijing China School of Electronic Information Engineering North China University of Technology Beijing China

ISBN: (纸本)9781665468893

3D reconstruction of object has raised much interest in the field of SAR. The feature of target at multi aspect angles can be obtained from sub-aperture images provided by circular SAR(CSAR), which is conducive to 3D reconstruction. Radargrammetric method is a conventional way to extract DEM while its performance for 3D reconstruction is limited by the image registration. It is difficult to find corresponding points in different sub-aperture images. In this paper, we proposed a method for 3D reconstruction of vehicle based on projections in sub-aperture images. According to the imaging mechanism of CSAR, different targets located on the same iso-range line in the zero doppler plane fall into the same cell. For a projection point, given a series of offsets, the projection point will be mapped inversely to the 3D mesh along the iso-range line. We can obtain candidates of the target. The intersection of iso-range lines can be regarded as voting process. For a candidate, the more times of intersection, the higher the number of votes, and the candidate point will be reserved. This fully excavates the information contained in the angle dimension of CSAR. The proposed approach is verified by the Gotcha Volumetric SAR Data Set.

关键词： Point cloud compression Space vehicles Image registration Three-dimensional displays Imaging Radar imaging Feature extraction

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Characteristics of Spatial and Temporal Vegetation Index Variability and Its Responses to Temperature and Precipitation in Mongolia

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Journal of Resources and Ecology 2024年第5期15卷 1175-1184页

作者： YANG Meihuan LI Yawen WANG Tao WANG Juanle LI Pengfei LI Ting HUANG Jing Ochir ALTANSUKH Davaadorj DAVAASUREN School of Geomatics and Geoinformation Xi’an University of Science and TechnologyXi’an 710054China Institute of Land and Space Xi’an University of Science and TechnologyXi’an 710054China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChineseAcademy of SciencesBeijing 100101China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China Department of Environment and Forest Engineering School of Engineering and Technology and Environmental Engineering LaboratoryNationalUniversity of MongoliaUlaanbaatar 14201Mongolia Department of Geography School of Art and SciencesNational University of MongoliaUlaanbaatar 14201Mongolia

The Mongolian Plateau,a vital ecological barrier in northern China,is of great importance for studying vegetation dynamics in Mongolia against the background of climate *** studies can enhance our understanding of regional vegetation responses to global warming and contribute to the establishment of a stronger ecological barrier in northern ***,we analyzed the spatial and temporal characteristics of the NDVI(normalized difference vegetation index)in Mongolia using 8 km resolution GIMMS NDVI3g data from 1990 to 2022,along with temperature,precipitation,and elevation *** analysis and correlation methods were used to examine the relationships between the NDVI and temperature,as well as *** results showed four important aspects of these relationships.(1)The NDVI in Mongolia increased significantly from 1990 to 2022 at a rate of 0.0015 yr^(-1)(P<0.05).(2)Mongolia’s NDVI increased from 1990 to 2022 in 60.73%of the *** this total,the area with a significant increase accounted for 31.67%and was concentrated on the eastern and western *** area experiencing a significant decrease accounted for 15.67%and was mainly located on the southwestern edges.(3)The NDVI analysis revealed significant increasing trends in all regions except for those at elevations of 1500-2000 *** greatest rate of increase was observed between 500 and 1000 m,and the increasing trend weakened as elevation continued to increase before gradually becoming significant ***,the NDVI increased significantly across different slopes,and the rate of increase decreased as the slope increased.(4)From 1990 to 2022,Mongolia’s NDVI was mostly negatively correlated with *** occurred over 66.75%of the total land area,with 17.21%of the region exhibiting a significant negative correlation,mainly in the ***,the NDVI demonstrated a positive correlation with precipitation,encompassing 86.71%of the total land *** 4

关键词： normalized difference vegetation index slope elevation trend analysis correlation analysis Mongolia

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Stacking, Strain-Engineering Induced Altermagnetism, Multipiezo Effect, and Topological State in Two-Dimensional Materials

arXiv

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arXiv 2025年

作者： Xun, Wei Liu, Xin Zhang, Youdong Wu, Yin-Zhong Li, Ping Jiangsu Engineering Research Center for Design and Application of Intelligent Detection Equipment School of Electronic Engineering Jiangsu Vocational College of Electronics and Information Huaian223003 China School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China State Key Laboratory for Mechanical Behavior of Materials Center for Spintronics and Quantum System School of Materials Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China State Key Laboratory for Surface Physics Department of Physics Fudan University Shanghai200433 China State Key Laboratory of Silicon and Advanced Semiconductor Materials Zhejiang University Hangzhou310027 China

Altermagnetism, as a newly identified form of unconventional antiferromagnetism, enables the removal of spin degeneracy in the absence of net magnetization that provides a platform for the low power consumption and ultra-fast device applications. However, the rare attention has been paid to the relationship between stacking, strain and altermagnet, multipiezo effect and topological state. Here, we propose a mechanism to realize the altermagnet, multipiezo effect, and topological state in two-dimensional materials by the stacking and strain engineering. Based on the analysis of symmetry, we find that the spin splitting feature related to the Ut, PTt, MzUt, or MzPTt symmetries in altermagnet multilayers. In addition, we find that the stacking engineering can effectively realize the transform from antiferromagnetism to altermagnetism and semiconductor to metal for the Jauns bilayer V2SeTeO. More interestingly, the strain not only induces an intriguing multipiezo effect, encompassing the piezovalley, piezomagnetism and piezoelectric, but also achieves the abundant topological phase. Our findings offer a generalized direction for manipulating the spin splitting, valley polarization, and topological states, promoting practical application of valleytronic and spintronic devices based on two-dimensional altermagnets. © 2025, CC BY.

关键词： Antiferromagnetism

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Research on Integrated Deployment Method of Digital Earth Thematic application Model 1

Research on Integrated Deployment Method of Digital Earth Th...

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1st China Digital Earth Conference, CDEC 2019

作者： Danpu, Zhao Xianqing, Tai Zhijie, Ma Key Laboratory of Technology in Geospatial Information Processing and Application System Institute of Elec-tronics Chinese Academy of Sciences Beijing100190 China Research Center of Intelligent City and Intelligent Transportation CAS R and D Center for Internet of Things Wuxi214135 China

Nowadays, the digital earth not only relates to the technologies of surveying and mapping geography, but also includes the analysis and cross-application of various scientific data related to geographic information. It requires a computing environment and management for the corresponding thematic application models which supports the online analysis on the digital earth. So we proposed an integrated deployment method for digital earth thematic application models based on container cloud, which provides a flexible and scalable information integration and analysis architecture for digital earth. Firstly, a Docker image building module is designed to facilitate the user to quickly build the application model into a image. Then the Docker technology is used to implement the containerization of the application model to provide a stable running environment for the application model. Finally, the Kubernetes is used to dynamically manage the cluster resources to realize rapid expansion of computing resources. In addition, the HDFS is deployed, and the image files and various scientific data respectively store with Hbase and Apache file systems. It can ensure the read and write speed of the application model and the related data and log files. It also used zabbix framework to manage the monitoring index of the container cloud cluster to ensure the execution effect of the application model. Through the tests of typical application model, it is verified that the method can integrated deploy and apply the cross-domain, hetero-architecture, multi-version and multi-class application models on the digital earth. © Published under licence by IOP Publishing Ltd.

关键词： Containers

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Examining the Fourier Spectrum of Speech Signal from a Time-Frequency Perspective for Automatic Depression Level Prediction

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IEEE Transactions on Affective Computing 2025年

作者： Niu, Mingyue Tao, Jianhua He, Yongjun Zhang, Shiqing Li, Ming YanShan University School of Information Science and Engineering Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province China Tsinghua University Department of Automation Bejing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Taizhou University Institute of Intelligent Information Processing Taizhou318000 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

Currently, many studies use Fourier amplitude spectra of speech signals to predict depression levels. However, those works often treat Fourier amplitude spectra as images or sequences to capture depression cues using convolutional neural networks or multilayer perceptrons. Therefore, they ignore the complex element composition and time-frequency attributes of Fourier spectra, which is not conducive to capturing the differences among individuals with different depression levels. For this reason, we construct a Time-Frequency Self-Embedding (TFSE) module, which not only stores the correlation relationship among real (imaginary) parts of Fourier spectra of different subjects from the time-frequency perspective, but also maintain the physical properties of data through the weight embedding process. Besides, Global Average Pooling (GAP) or linear layers are difficult to balance both temporal and frequency dimensions in the vectorization process. Therefore, we construct a Time-Frequency Tensor Vectorization (TFTV) module, which summarizes each channel along time and frequency dimensions, and then generates the vectorization result by integrating various channels. In this way, we combine TFSE and TFTV modules to form our SpectrumFormer model for predicting depression levels. Evaluation indicators on AVEC 2013 and AVEC 2014 depression databases imply the progressiveness of our model. © 2010-2012 IEEE.

关键词： Depression level prediction Real and imaginary parts Tensor vectorization Time-frequency attributes

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引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoke Zhu Danyang Li Xiaopan Chen Fumin Qi Fan Zhang Xiao-Yuan Jing School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China Department of System Operation National Supercomuting Center in Shenzhen Shenzhen China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Facial image based kinship verification aims to decide whether there exists kinship between the given facial images. In practice, the cross-generation differences will cause adverse effects on kinship verification, which limits the performance. Therefore, how to mine the implied similarity from facial images with large cross-generation divergence is an important problem in kinship verification, which has not yet been well studied. In view of this, we propose a Similarity Mining via Implicit matching pattern LEarning (SMILE) approach for kinship verification. Specifically, SMILE mainly consists of two modules, including a Semi-coupled Multi-pattern Similarity Learning (SMSL) module and a Cross-Generation Feature Normalization (CGFN) module. The SMSL module is designed to learn multiple semi-coupled matching patterns for mining the implicit facial similarity information from different perspectives. The CGFN module aims to reduce the divergence between facial images of parent and child. Extensive experiments demonstrate that the proposed approach outperforms the existing state-of-the-art methods.

关键词： Pattern matching Similarity learning

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ATASI-Net: An Efficient Sparse Reconstruction Network for Tomographic SAR Imaging with Adaptive Threshold

arXiv

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arXiv 2022年

作者： Wang, Muhan Zhang, Zhe Qiu, Xiaolan Gao, Silin Wang, Yue Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology China Suzhou Aerospace Information Research Institute Jiangsu Suzhou China Aerospace Information Research Institute Chinese Academy of Sciences China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China The Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Electrical and Computer Engineering Department George Mason University FairfaxVA United States

Tomographic SAR technique has attracted remarkable interest for its ability of three-dimensional resolving along the elevation direction via a stack of SAR images collected from different cross-track angles. The emerged compressed sensing (CS)-based algorithms have been introduced into TomoSAR considering its super-resolution ability with limited samples. However, the conventional CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity, and complex parameter fine-tuning. Aiming at efficient TomoSAR imaging, this paper proposes a novel efficient sparse unfolding network based on the analytic learned iterative shrinkage thresholding algorithm (ALISTA) architecture with adaptive threshold, named Adaptive Threshold ALISTA-based Sparse Imaging Network (ATASI-Net). The weight matrix in each layer of ATASI-Net is pre-computed as the solution of an offline optimization problem, leaving only two scalar parameters to be learned from data, which significantly simplifies the training stage. In addition, adaptive threshold is introduced for each azimuth-range pixel, enabling the threshold shrinkage to be not only layer-varied but also element-wise. Moreover, the final learned thresholds can be visualized and combined with the SAR image semantics for mutual feedback. Finally, extensive experiments on simulated and real data are carried out to demonstrate the effectiveness and efficiency of the proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Multispectral Pansharpening Based on High-Pass Modulation Injection Model with Difference Factor

Multispectral Pansharpening Based on High-Pass Modulation In...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yiyang Su Hongyu Yao Peng Wang Xiwang Zhang Jue Wang College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Southeast Coast Marine Information Intelligent Perception and Application Ministry of Natural Resources Zhangzhou Institute of Surveying and Mapping Zhangzhou China Fujian Provincial Key Lab of Coastal Basin Environment Fujian Polytechnic Normal University Fuqing China Donghai Laboratory Zhoushan China Anhui Province Key Laboratory of Physical Geographic Environment Chuzhou University Chuzhou China Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions Ministry of Education Henan University Kaifeng China Rady School of Management University of California San Diego California USA

This paper proposes a multispectral pansharpening based on high-pass modulation injection model with difference factor (HPM-DF), which is dedicated to solving the problem of difference between multispectral image and panchromatic image acquired at different moments. In the high-pass modulation injection model, we introduce a difference factor and use the alternating direction method of multipliers (ADMM) to fully analyze the difference variability to derive the final fusion product. Experiments assessed at both reduced and full resolution show that the proposed method can acquire better performance than the traditional pansharpening methods.

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Poissonian Hyperspectral Image Denoising without Using Anscombe Transform

Poissonian Hyperspectral Image Denoising without Using Ansco...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yulan Wang Peng Wang Xiwang Zhang Jue Wang Matthieu Muller College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Beijing Key Laboratory of Urban Spatial Information Engineering Beijing Institute of Surveying and Mapping Beijing China Key Laboratory of Land Satellite Remote Sensing Application Ministry of Natural Resources Jiangsu Province Surveying and Mapping Engineering Institute Nanjing China Xi’an Key Laboratory of Territorial Spatial Information Chang'an University Xi’an China Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions Ministry of Education Henan University Kaifeng China Rady School of Management University of California San Diego California USA CNRS Grenoble INP GIPSA-lab University Grenoble Alpes Grenoble France

In the most existing Hyperspectral Image (HSI) denoising methods, Poisson noise is first transformed into Gaussian noise through Anscombe transform and then remove it. However, transform errors may occur that affect the final denoising results when using Anscombe transform. In this paper, we propose a Poissonian hyperspectral image denoising method without using Anscombe transform (WUAT) to directly remove the noise of Poissonian HSI under the maximum a posteriori (MAP) model by finding the minimum value of the negative logarithmic Poisson log-likelihood combined with the total variation (TV). The experimental results show that the proposed method can acquire better performance than most state-of-the-art denoising methods.

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