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A Grad-CAM and capsule network hybrid method for remote sensing image scene classification

Frontiers of Earth Science 2024年第3期18卷 538-553页

作者： Zhan HE Chunju ZHANG Shu WANG Jianwei HUANG Xiaoyun ZHENG Weijie JIANG Jiachen BO Yucheng YANG shenzhen data management center of planning and natural resources Key Laboratory of Urban Land Resources Monitoring and Simulation(Ministry of Natural Resources)Shenzhen 518000China Key Laboratory of Jianghuai Arable Land resources Protection and Eco-restoration(Ministry of natural resources) Hefei 230088China State Key Laboratory of resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China School of Civil Engineering Hefei University of TechnologyHefei 230009China

Remote sensing image scene classification and remote sensing technology applications are hot research *** CNN-based models have reached high average accuracy,some classes are still misclassified,such as“freeway,”“spare residential,”and“commercial_area.”These classes contain typical decisive features,spatial-relation features,and mixed decisive and spatial-relation features,which limit high-quality image scene *** address this issue,this paper proposes a Grad-CAM and capsule network hybrid method for image scene *** Grad-CAM and capsule network structures have the potential to recognize decisive features and spatial-relation features,*** using a pre-trained model,hybrid structure,and structure adjustment,the proposed model can recognize both decisive and spatial-relation features.A group of experiments is designed on three popular data sets with increasing classification *** the most advanced experiment,92.67%average accuracy is ***,83%,75%,and 86%accuracies are obtained in the classes of“church,”“palace,”and“commercial_area,”*** research demonstrates that the hybrid structure can effectively improve performance by considering both decisive and spatial-relation ***,Grad-CAM-CapsNet is a promising and powerful structure for image scene classification.

关键词： image scene classification CNN Grad-CAM CapsNet DenseNet

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Integrated data-model-knowledge representation for natural resource entities

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International Journal of Digital Earth 2022年第1期15卷 653-678页

作者： Yulin Ding Zhaowen Xu Qing Zhu Hankan Li Yan Luo Ying Bao Lingjun Tang Sen Zeng Faculty of Geosciences and Environmental Engineering Southwest Jiaotong UniversityChengduPeople's Republic of China Municipal planning & Land Estate Information center Shenzhen Municipal Bureau of Planning and Natural ResourcesShenzhenPeople's Republic of China Information center Department of Natural Resources of Sichuan ProvinceChengduPeople's Republic of China

The unified management and planning of national or provincial natural resources distributed both aboveground and underground have become increasingly *** depictions of natural resource elements and their interactions are key to achieving integrated and systematic management of natural ***,current spatiotemporal data models are based only on data descriptions,attribute records,and other model knowledge of a more general basis,without intuitively describing relationships between these elements and natural *** paper,therefore,proposes an integrated data-model-knowledge representation model to explicitly describe the time,space,and interaction of natural resource entities through an integrated knowledge ***,this study constructs a conceptual model using the aspects of semantics,scale,and data-model-knowledge,thereby explicitly describing the relationships of natural ***,a logical model of natural resource representation is proposed,that is integrated with time,space,attributes,and ***,taking the management of water resources as an example,this paper realizes the meticulous presentation of the levels of detail and rich semantic relations of natural resource *** findings of this study lay the foundation for a more efficient,precise,and lucid perception of the distribution laws and complicated interactional relationships of natural resources,both aboveground and underground.

关键词： natural resources data-model-knowledge interactional relationships

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CW-YOLO: joint learning for mask wearing detection in low-light conditions

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Frontiers of Computer Science 2023年第6期17卷 191-193页

作者： Mingqiang GUO Hongting SHENG Zhizheng ZHANG Ying HUANG Xueye CHEN Cunjin WANG Jiaming ZHANG School of Computer Science China University of GeosciencesWuhan 430074China National Engineering Research center of Geographic Information System Wuhan 430074China Wuhan Zondy Cyber Technology Co.Ltd. Wuhan 430074China shenzhen data management center of planning and natural resources Shenzhen 518000China Key Laboratory of Urban Land resources Monitoring and Simulation(Ministry of natural resources) Shenzhen 518000China College of Engineering Boston UniversityBoston 02215USA

1 Introduction With the rapid progress of Artificial Intelligence(AI)technology in object detection and face recognition,deep learning methods for face mask wearing detection have become increasingly mature and continuously take into account the needs of efficiency and ***,these conventional detection methods mostly ignore the complexity of real-world application scenarios,such as extremely darkness and gloomy *** unfavorable conditions lead to a series of image degradations that seriously hamper machine vision *** camera parameter adjustment,auxiliary lighting,or pre-processing enhancement[1]can weaken these negative effects to some extent to promote the detection,they will also result in increased hardware and time costs.

关键词： hardware lighting weather

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Spatial downscaling of climate variables using three statistical methods in Central Iran

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Journal of Mountain Science 2018年第3期15卷 606-617页

作者： Zahra JABERALANSAR Mostafa TARKESH Mehdi BASSIRI Department of natural resources Isfahan University of Technology Research Division of natural resources Isfahan Agricultural and Natural Resources Research and Education CenterAREEO

Spatial downscaling methods are widely used for the production of bioclimatic variables(e.g. temperature and precipitation) in studies related to species ecological niche and drainage basin management and planning. This study applied three different statistical methods, i.e. the moving window regression(MWR), nonparametric multiplicative regression(NPMR), and generalized linear model(GLM), to downscale the annual mean temperature(Bio1) and annual precipitation(Bio12) in central Iran from coarse scale(1 km × 1 km) to fine scale(250 m ×250 m). Elevation, aspect, distance from sea and normalized difference vegetation index(NDVI) were used as covariates to create downscaled bioclimatic variables. Model assessment was performed by comparing model outcomes with observational data from weather stations. Coefficients of determination(R2), bias, and root-mean-square error(RMSE) were used to evaluate models and covariates. The elevation could effectively justify the changes in bioclimatic factors related to temperature and precipitation. Allthree models could downscale the mean annual temperature data with similar R2, RMSE, and bias values. The MWR had the best performance and highest accuracy in downscaling annual precipitation(R2=0.70; RMSE=123.44). In general, the two nonparametric models, i.e. MWR and NPMR, can be reliably used for the downscaling of bioclimatic variables which have wide applications in species distribution modeling.

关键词： Statistical models Climatic data Elevation Spatial resolution Temperature Precipitation

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Multiscale simulation of the urban wind environment under typhoon weather conditions

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Building Simulation 2023年第9期16卷 1713-1734页

作者： Zihan Zhao Yiqing Xiao Chao Li P.W.Chan Gang Hu Qingfeng Zhou School of Construction Engineering Shenzhen PolytechnicShenzhenChina School of Civil and Environmental Engineering Harbin Institute of Technology ShenzhenShenzhenChina Hong Kong Observatory KowloonHong KongChina shenzhen Urban planning&Land resources Research center ShenzhenGuangdong 518055China

Accurate simulations of planetary boundary layer(PBL)winds in urban areas require combining meteorological knowledge and fine-grained geometrical *** fluid dynamics(CFD)is widely used to assess pedestrian wind comfort and wind disasters in planning resilient ***,the CFD-predicted PBL is highly affected by the inflow *** profiles under extreme weather conditions,such as tropical cyclones,can hardly be determined,and associated uniform logarithmic or power law expressions have not been *** this study,urban wind flow over mountainous terrain was simulated using a one-way nested simulation approach between mesoscale and microscale *** inflow wind speed,turbulence scalars,and potential temperature in the CFD code are sustained by the numerical weather prediction(NWP)*** considering typhoon weather conditions were examined to improve the numerical accuracy in determining mesoscale typhoon structures and pedestrian-level wind *** numerical errors were quantified in mesoscale and microscale formulations.A new tendency assimilation was proposed by incorporating local-scale observations into the CFD *** approach entailed empirical mode decomposition to quantify the mean wind speed differences between the observations and NWP results,which were then extrapolated to NWP-CFD nested interfaces via multiplication by the spatial correlation *** numerical performance was validated against both on-site observations for meteorological purposes and wind profiles retrieved from the experimental LiDAR of the landfalling typhoon *** simulated wind field exhibited an increased accuracy in the local urban *** specifically,the index of agreement in wind speeds was improved from 0.28 to 0.72,and the mean absolute errors were reduced from 5.46 m/s to 1.89 m/s.

关键词： NWP-CFD urban wind flow data assimilation typhoon disaster resilient cities

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China's food security situation and key questions in the new era:A perspective of farmland protection

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Journal of Geographical Sciences 2022年第6期32卷 1001-1019页

作者： LIANG Xinyuan JIN Xiaobin HAN Bo SUN Rui XU Weiyi LI Hanbing HE Jie LI Jin School of Geography and Ocean Science Nanjing UniversityNanjing 210023China Key Laboratory of Coastal Zone Exploitation and Protection Ministry of Natural ResourcesNanjing 210023China natural resources Research center Nanjing UniversityNanjing 210023China Wuhan planning and Research Institute Wuhan 430014China Pinggang Middle School Shenzhen 518116GuangdongChina

As the world’s largest developing country,the ability of China’s agricultural resource utilization to effectively support the current and future food security goals has been affected by a variety of factors(e.g.,transformed supply channels,tightening international situation and frequent emergencies)in recent years and has attracted extensive attention from the academic community subject to multiple *** study uses literature review,statistical analysis,and spatial analysis methods to systematically explore China’s food security situation in the context of farmland resource *** is found that the demand-side pressures such as demographic changes,social class differentiation,and dietary structure adjustments derived from economic growth and rapid urbanization have placed extremely high expectations on food ***,the quantitative restrictions,utilization ways,and health risks of farmland resources on the supply side constitute a huge hidden concern that affects the stability of food *** China’s farmland protection system is undergoing a transition from focusing on quantity management to sustainable use,the matching and coordinating demand pressure and supply capacity for food security is ***,facing uncertain future development scenarios,policymakers should focus on building a resilient space for China’s farmland protection to withstand the interference of major *** existing farmland protection space policy can be integrated by establishing a national farmland strategic reserve system(based on resilient space),and further development of targeted use control measures for zoning,grading,and classification will help realize sustainable China’s farmland resources use.

关键词： food security systematical analysis key questions farmland protection resilient space China

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Construction of Smart City Spatio-Temporal Information Cloud Platform in Weifang,China

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Journal of Donghua University(English Edition) 2019年第6期36卷 615-622页

作者： LIU Qianzhong LIU Xiaojing ZHAO Pingting Weifang natural resources and planning Bureau Weifang 261041China

On the basis of the digital Weifang geospatial framework,Smart Weifang spatio-temporal information cloud platform(WFCP)integrated legal person information,population,place name and address data,macroeconomic data and so *** it also expanded the data contents,such as the indoor and outdoor data,the overground and underground data,panoramic data and real *** also introduced the contents of historical geographical information in different periods and real-time location information,address information of sensing equipment,real-time perception and interpreting *** has overcome the difficulties of real-time access of Internet of Things(IoT)perception,multi-node collaboration,64-bit support,cluster deployment and has the characteristics of spatio-temporal management,ondemand service,large data analysis and micro-service *** built spatio-temporal information big data center and spatio-temporal information cloud platform,realized the convergence and management of the distributed big data,deeply applied for land,transportation,environmental protection,police and subdistrict five areas,by supporting the integrated application of multi-source information and supporting intelligent deep *** the aspect of hardware environment construction,according to the top-level design and unified arrangement of Smart Weifang,the WFCP was migrated to Weifang cloud computing center,to achieve the on-demand computing resources and dynamic scheduling load-based computing resources,to support the generalizing load map application.

关键词： spatio-temporal information geospatial framework dataset Html5 technology NewMap spatio-temporal data engine spatio-temporal big data center

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Multimorbidity and mortality among older patients with coronary heart disease in shenzhen,China

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Journal of Geriatric Cardiology 2024年第1期21卷 81-89页

作者： Fu-Rong LI Shuang WANG Xia LI Zhi-Yuan CHENG Cheng JIN Chun-Bao MO Jing ZHENG Feng-Chao LIANG Dong-Feng GU shenzhen Key Laboratory of Cardiovascular Health and Precision Medicine Southern University of Science and TechnologyShenzhenGuangdongChina School of Public Health and Emergency management Southern University of Science and TechnologyShenzhenGuangdongChina shenzhen Health Development Research and data management center ShenzhenChina School of Medicine Southern University of Science and TechnologyShenzhenGuangdongChina

BACKGROUND The current understanding of the magnitude and consequences of multimorbidity in Chinese older adults with coronary heart disease(CHD)is *** aimed to assess the association and population-attributable fractions(PAFs)between multimorbidity and mortality among hospitalized older patients who were diagnosed with CHD in shenzhen,*** We conducted a retrospective cohort study of older Chinese patients(aged≥65 years)who were diagnosed with *** proportional hazards models were used to estimate the associations between multimorbidity and all-cause and cardiovascular disease(CVD)*** also calculated the *** The study comprised 76,455 older hospitalized patients who were diagnosed with CHD between January 1,2016,and August 31,*** them,70,217(91.9%)had multimorbidity,defined as the presence of at least one of the predefined 14 chronic *** with cancer,hemorrhagic stroke and chronic liver disease had the worst overall death risk,with adjusted HRs(95%CIs)of 4.05(3.77,4.38),2.22(1.94,2.53),and 1.85(1.63,2.11),*** CVD mortality,the highest risk was observed for hemorrhagic stroke,ischemic stroke,and chronic kidney disease;the corresponding adjusted HRs(95%CIs)were 3.24(2.77,3.79),1.91(1.79,2.04),and 1.81(1.64,1.99),***-cause mortality was mostly attributable to cancer,heart failure and ischemic stroke,with PAFs of 11.8,10.2,and 9.1,*** for CVD mortality,the leading PAFs were heart failure,ischemic stroke and diabetes;the corresponding PAFs were 18.0,15.7,and 6.1,*** Multimorbidity was common and had a significant impact on mortality among older patients with CHD in shenzhen,***,heart failure,ischemic stroke and diabetes are the primary contributors to ***,prioritizing improved treatment and management of these comorbidities is essential for the survival prognosis of CHD patients from a holistic public health perspective.

关键词： patients morbidity mortality

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Outlier Detection for Water Supply data Based on Joint Auto-Encoder

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Computers, Materials & Continua 2020年第7期64卷 541-555页

作者： Shu Fang Lei Huang Yi Wan Weize Sun Jingxin Xu Guangdong Laboratory of Artificial-Intelligence and Cyber-Economics(SZ) College of Electronics and Information EngineeringShenzhen UniversityShenzhen518061China Water resources management center of Ministry of Water resources BeijingChina Departmet of Housing and Public Works QueenslandAustralia

With the development of science and technology,the status of the water environment has received more and more *** this paper,we propose a deep learning model,named a Joint Auto-Encoder network,to solve the problem of outlier detection in water supply *** Joint Auto-Encoder network first expands the size of training data and extracts the useful features from the input data,and then reconstructs the input data effectively into an *** outliers are detected based on the network’s reconstruction errors,with a larger reconstruction error indicating a higher rate to be an *** water supply data,there are mainly two types of outliers:outliers with large values and those with values closed to *** set two separate thresholds,and,for the reconstruction errors to detect the two types of outliers *** data samples with reconstruction errors exceeding the thresholds are voted to be *** two thresholds can be calculated by the classification confusion matrix and the receiver operating characteristic(ROC)*** have also performed comparisons between the Joint Auto-Encoder and the vanilla Auto-Encoder in this paper on both the synthesis data set and the MNIST data *** a result,our model has proved to outperform the vanilla Auto-Encoder and some other outlier detection approaches with the recall rate of 98.94 percent in water supply data.

关键词： Water supply data outlier detection auto-encoder deep learning

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Review of constraints and critical success factors of developing urban underground space

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Underground Space 2023年第5期12卷 137-155页

作者： Yani Lai Yuling Wang Jing Cheng Xiangsheng Chen Quan Liu College of Civil and Transportation Engineering Shenzhen UniversityShenzhen 518055China LAY-OUT planning Consultants Co. Ltd.TAGEN Knowledge&Innovation CenterShenzhen 518034China

The development of urban underground space(UUS)is of great significance for sustainable urban development because it has potential to solve many urban ***,the development of UUS is limited because there are still a lot of constraints and *** better understand the constraining factors of developing UUS and improve the development and utilization of UUS,this study conducts a comprehensive survey of existing literature retrieved from the Web of Science Core Collection *** main constraining factors,including the limited geological conditions,high cost,lack of natural light and ventilation,high incidence,and difficulty in collecting data and information on UUS,have been discussed to better understand how they affect the development of *** on the survey,the critical success factors of developing UUS,such as the effective governance structure,planning system for integrating UUS into urban development,coordination of the design of ground and underground,delineation of land property rights of UUS,and application of information techniques in the use of UUS,have been identified and *** addition,knowledge gaps in the studies on development of UUS have been ***,the research trends and future directions have been proposed for better understanding and practice of UUS development.

关键词： Urban underground space Constraining factor Critical success factor resources Metro Energy Infrastructure

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