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

Abstract

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.

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

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”