Deep Learning Image Feature Recognition Algorithm for Judgment on the Rationality of Landscape Planning and Design

Abstract

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.

Expanding the Associations between Landscape Characteristics and Aesthetic Sensory Perception for Traditional Village Public Space

“…The recognition accuracy of this method is affected by the resolution of remote sensing data, the selection of classification algorithms, and the accuracy of feature extraction methods [34,35]. The identification of microscale landscape elements is mainly performed by using machine learning and model training based on magnanimous photographs, such as micro-scale landscape element recognition which mainly uses machine learning and model training, such as supporting vector machine (SVM), decision tree, convolutional neural network (CNN), and other models to realize automatic extraction of element categories [36,37]. The recognition accuracy of this class of methods depends on the quality of the features, models, and applied training data.…”

Research on Image Recognition Methods Based on Deep Learning

“…In daily life, the automatic alarm function of surveillance is a security mechanism for public safety [12]. To summarize, the research on image recognition problems has important market prospects and practical application value [13][14].…”