Abstract
With the gradual improvement of the quality of life, taste, and ecological and environmental awareness of urban residents in China, the environmental landscape of residential areas has gradually become a hot spot. At present, the level of the residential environmental landscape has become a necessary means for real estate developers to publicize products and improve economic benefits. Although many residential areas have invested a high cost in constructing environmental landscapes, there are always some deficiencies and defects in the design and implementation of environmental landscapes in residential areas due to various reasons. Therefore, to ameliorate the low efficiency and high cost of manual processing of landscape images, a Fully Convolutional Network (FCN) model based on the traditional Convolutional Neural Network (CNN) is designed for semantic segmentation of landscape images to deal with the excessive amount of landscape elements in landscape image processing. The deconvolution method is utilized to realize pixel-level semantic segmentation. Besides, the image preprocessing method enhances the data to prevent overfitting from commonly occurring in FCN. Moreover, the model two-stage training method ameliorates long training time and complex convergence in deep learning. Finally, three upsampling network structures, i.e., FCN-32s, FCN-16s, and FCN-8s, are selected for a comparative experiment to determine the most suitable network. The experimental results demonstrate that the FCN-8s upsampling network structure is the most prominent; it attains a pixel accuracy of more than 90%, an average accuracy of 88%, and an average Image Understanding of 75%. The three values are the highest among the three upsampling structures, indicating that the FCN-8s can realize accurate landscape image processing. Besides, the recognition accuracy of FCN for landscape elements reaches 90%, 25% higher than that of CNN. This method is effective and accurate in classifying landscape elements, improves the classification accuracy intelligently, and significantly reduces the cost of landscape element classification, which is feasible.
Highlights
With the application of computer science to landscape architecture, landscape architecture design method tends to be of high-efficiency, fast, accurate, variegated, and easy to modify, bringing the science of landscape architecture to a new era [1]
E information in the landscape image is input into the landscape element recognition system through computer programming to automatically recognize landscape elements [13]. e core part of digital image processing is image recognition. e specific image recognition process contains feature extraction of landscape images after preprocessing, some standards used to classify landscape elements on images, and completing the task of identifying landscape elements [14]
Among these three upsampling structures, the Fully Convolutional Network (FCN)-8s structure achieves the highest pixel accuracy, average accuracy, and average IU value. e pixel accuracy is more than 90%, the average accuracy is more than 88%, and the average IU value is more than 75%. is model has good adaptability and high accuracy for various landscape elements in landscape images
Summary
With the application of computer science to landscape architecture, landscape architecture design method tends to be of high-efficiency, fast, accurate, variegated, and easy to modify, bringing the science of landscape architecture to a new era [1]. Image recognition technology appeared in the middle of the 20th century and was first applied in aerospace exploration. It mainly went through three growth stages: text recognition, Wireless Communications and Mobile Computing digital image processing/recognition, and object recognition [6]. To achieve a people-oriented landscape, the designers should perform a thoughtful and humane design by fully understanding the residents’ age structure, occupation, life, work habits, and physical requirements. In this way, residential landscaping and leisure facilities can respect and consider every detail of human activities and enable the residents to feel the comfort of a humane space
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