This paper uses an improved deep learning algorithm to judge the rationality of the design of landscape image feature recognition. The preprocessing of the image is proposed to enhance the data. The deficiencies in landscape feature extraction are further addressed based on the new model. Then, the two-stage training method of the model is used to solve the problems of long training time and convergence difficulties in deep learning. Innovative methods for zoning and segmentation training of landscape pattern features are proposed, which makes model training faster and generates more creative landscape patterns. Because of the impact of too many types of landscape elements in landscape images, traditional convolutional neural networks can no longer effectively solve this problem. On this basis, a fully convolutional neural network model is designed to perform semantic segmentation of landscape elements in landscape images. Through the method of deconvolution, the pixel-level semantic segmentation is realized. Compared with the 65% accuracy rate of the convolutional neural network, the fully convolutional neural network has an accuracy rate of 90.3% for the recognition of landscape elements. The method is effective, accurate, and intelligent for the classification of landscape element design, which better improves the accuracy of classification, greatly reduces the cost of landscape element design classification, and ensures that the technical method is feasible. This paper classifies landscape behavior based on this model for full convolutional neural network landscape images and demonstrates the effectiveness of using the model. In terms of landscape image processing, the image evaluation provides a certain basis.
T HE generation of huge amounts of data, called big data, is creating the need for efficient tools to manage those data.Artificial intelligence (AI) has become the powerful tool in dealing with big data with recent breakthroughs at multiple fronts in machine learning, including deep learning. Meanwhile, information networks are becoming larger and more complicated, generating a huge amount of runtime statistics data such as traffic load, resource usages. The emerging big data and AI technologies may include a bunch of new requirements, applications and scenarios such as e-health, Intelligent Transportation Systems (ITS), Industrial Internet of Things (IIoT), and smart cities in the term of computing networks. The big data and AI driven network technologies also provide an unprecedented patient to discover new features, to characterize user demands and system capabilities in network resource assignment, security and privacy, system architecture, modeling and applications, which needs more explorations. The focus of this special section is to address the big data and artificial intelligence for network technologies.We appreciate contributions to this special section and the valuable and extensive efforts of the reviewers. The topics of this special section range from big data and AI algorithms, models, architecture for networks and systems to network architecture, automation, and service based on big data and AI. A brief review follows:In "Application Behaviors Driven Self-Organizing Network (SON) for 4G LTE Networks," Ouyang et al. present an application characteristics-driven self-optimization system, APP-SON, to optimize 4G/5G network performance and user Quality of Experience using big data platform. In "DCAuth: Data-Centric Authentication for Secure In-Network Big-Data Retrieval," Li et al. present a novel data-centric authentication scheme for secure in-network bigdata retrieval. Xu et al. present a novel learning-based dynamic resource provisioning for network slicing in their article "Learning-Based Dynamic Resource Provisioning for Network Slicing with Ensured End-to-End Performance Bound." In "When Crowd Meets Big Video Data: Cloud-Edge Collaborative Transcoding for Personal Livecast," Zhu et al. propose a novel low-latency, cost-efficient mechanisms for transcoding big video data in the personal livecast applications. In "Trustworthy Website Detection Based on Social Hyperlink Network Analysi," Niu et al. present an enhanced OpinionWalk (EOW) algorithm to compute the trustworthiness of all websites and identify trustworthy websites with higher trust values based on social hyperlink network analysis. He et al. present a novel deep reinforcement learning approach to automatically make a decision for optimally allocating the network resources for social networks in "Trust-Based Social Networks With Computing, Caching and Communications: A Deep Reinforcement Learning Approach." In "Rethinking Behaviors and Activities of Base Stations in Mobile Cellular Networks Based on Big Data Analysis," Jiang et al. prese...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.