An SOE-Based Learning Framework Using Multisource Big Data for Identifying Urban Functional Zones

Feng, Ying; Huang, Zhou; Wang, Yaoli; Wan, Lin; Liu, Yu; Zhang, Yi; Shan, Xv

doi:10.1109/jstars.2021.3091848

Cited by 36 publications

(19 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhou et al considered the integration of data sets from two different places, established a linear regression model in the case of small samples, and proposed hypothesis testing in the case of small samples. And combined with real datasets, it is demonstrated that fusing together similar datasets from multiple sites improves the statistical power of the model [ 10 ]. Zhou et al used a graphical model approach to address the problem of distribution transfer in different datasets and proposed a hypothesis test under which conditions such datasets can be fused or analyzed.…”

Section: Methodsmentioning

confidence: 99%

Urban Landscaping Landscape Design and Maintenance Management Method Based on Multisource Big Data Fusion

Zhu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In the process of continuous urbanization construction, the construction scale of urban landscaping projects is getting larger. At the same time, the design and the maintenance of the management is becoming more important. Recently, the rocketing development of the ternary world of many people, machines, and things has triggered the generation of multisource fusion data and the development of artificial intelligence technology, and the world has entered the era of multisource big data intelligence. Multisource data refer to the fusion of multiple types of data with effective characteristic information, which has richer, more comprehensive, more detailed, and more effective information than a single data source, and can provide high-quality data sources for various complex problems. Therefore, more effective data can be provided for the definition of urban fringe areas. From the moment Google’s AlphaGo defeated Go world champion Li Shishi, the chess game has been occupied by AI, setting off an upsurge in the study, research, and application of AI technology. Colleges and universities around the world have followed suit and set up AI-related majors. Deep learning is one of the cutting-edge technologies in the field of artificial intelligence. It is a method to solve complex real-life problems by extracting effective information from the data and mining key features on the basis of a large amount of learning and computing data.

show abstract

Section: Methodsmentioning

confidence: 99%

Urban Landscaping Landscape Design and Maintenance Management Method Based on Multisource Big Data Fusion

Zhu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…e power grid company announced the smart grid plan for the first time, that is, the comprehensive construction of a strong grid with a UHV grid as the backbone and coordinated development of power grids at all levels as the basis, and a strong smart grid characterized by informatization, automation, and interaction [5]. In the report on the development of modern power grids, the Energy Administration summarizes the seven characteristics of smart grids: it has the ability to self-repair, stimulate the initiative of users to participate in the operation of the power grid, the security ability to resist attacks, the high quality of electric power, accommodate various forms of power generation and storage, prosper the power market, optimize equipment operation, and reduce power grid operating costs [6]. Eladl and others believed that the effective combination of smart grid and Internet of ings technology can improve the stability of the power system and the efficiency of energy use and achieve sustainable energy development [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Big Data and Internet of Things Technology in the Management of Financial Operating Income of Electric Power Enterprises

2022

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

Aim. In order to effectively integrate and rationally allocate the complex information flow of people, finance, and materials in the daily operation of the enterprise and improve the daily operation efficiency and management effect of the enterprise, the author proposes a method for the application of big data and Internet of Things technology in the management of financial operating income of electric power enterprises. The method specifically includes an overview of the relevant theories of financial management based on ERP and an analysis of the needs of a power company’s financial management system based on ERP so as to construct an evaluation index system for the financial management system. The experimental results show that the return on assets of the experimental enterprises has increased from 27.48% in 2015 to 55.18% in 2019, an increase of 27.7%, and the profitability has been continuously enhanced. In 2019, the company achieved a net profit of 392 million yuan, an increase of about 4 times compared with the previous 77 million yuan in 2015. Conclusion. The application can further optimize the allocation of various resources of the enterprise and ensure the effective implementation of the long-term strategy and management goals of the enterprise on the basis of realizing the management needs of the enterprise.

show abstract

“…As the major sites of human activities, differences between the physical features of buildings can often reflect the functions of areas [ 22 , 36 ]. Many studies have confirmed that building features do have the potential to distinguish functional urban areas [ 37 , 38 ].…”

Section: Methodsmentioning

confidence: 99%

Sub-Block Urban Function Recognition with the Integration of Multi-Source Data

Liu

Deng

et al. 2022

Sensors

View full text Add to dashboard Cite

The recognition of urban functional areas (UFAs) is of great significance for the understanding of urban structures and urban planning. Due to the limitation of data sources, early research was characterized by problems such as singular data, incomplete results, and inadequate consideration of the socioeconomic environment. The development of multi-source big data brings new opportunities for dynamic recognition of UFAs. In this study, a sub-block function recognition framework that integrates multi-feature information from building footprints, point-of-interest (POI) data, and Landsat images is proposed to classify UFAs at the sub-block level using a random forest model. The recognition accuracies of single- and mixed-function areas in the core urban area of Guangzhou, China, obtained by this framework are found to be significantly higher than those of other methods. The overall accuracy (OA) of single-function areas is 82%, which is 8–36% higher than that of other models. The research conclusions show that the introduction of the three-dimensional (3D) features of buildings and finer land cover features can improve the recognition accuracy of UFAs. The proposed method that uses open access data and achieves comprehensive results provides a more practical solution for the recognition of UFAs.

show abstract

An SOE-Based Learning Framework Using Multisource Big Data for Identifying Urban Functional Zones

Cited by 36 publications

References 42 publications

Urban Landscaping Landscape Design and Maintenance Management Method Based on Multisource Big Data Fusion

Urban Landscaping Landscape Design and Maintenance Management Method Based on Multisource Big Data Fusion

Application of Big Data and Internet of Things Technology in the Management of Financial Operating Income of Electric Power Enterprises

Sub-Block Urban Function Recognition with the Integration of Multi-Source Data

Contact Info

Product

Resources

About