Quantitative Evaluation of Plant and Modern Urban Landscape Spatial Scale Based on Multiscale Convolutional Neural Network.

Yang Wang,Moyang Li,Syed Hassan Ahmed

doi:10.1155/2021/7742700

Abstract

Modern urban landscape is a simple ecosystem, which is of great significance to the sustainable development of the city. This study proposes a landscape information extraction model based on deep convolutional neural network, studies the multiscale landscape convolutional neural network classification method, constructs a landscape information extraction model based on multiscale CNN, and finally analyzes the quantitative effect of deep convolutional neural network. The results show that the overall kappa coefficient is 0.91 and the classification accuracy is 93% by calculating the confusion matrix, production accuracy, and user accuracy. The method proposed in this study can identify more than 90% of water targets, the user accuracy and production accuracy are 99.78% and 91.94%, respectively, and the overall accuracy is 93.33%. The method proposed in this study is obviously better than other methods, and the kappa coefficient and overall accuracy are the best. This study provides a certain reference value for the quantitative evaluation of modern urban landscape spatial scale.

Highlights

With the rapid development of urbanization in China, the landscape area is shrinking, which has a serious impact on the sustainable development of ecology in China [1]
Remote sensing image technology has been widely used in landscape resources monitoring due to its advantages of wide coverage, fast acquisition speed, and large amount of information data [3]. e key of plant garden monitoring technology is the extraction of remote sensing images
This paper proposes a model of plant and modern urban landscape spatial scale evaluation based on deep convolutional neural network, which can provide some reference and reference for urban landscape planning

Summary

Introduction

With the rapid development of urbanization in China, the landscape area is shrinking, which has a serious impact on the sustainable development of ecology in China [1]. E multiscale CNN spatial feature elements are obtained as shown in the following equation:. Remote sensing training image data selection 32 × 32, 64 × 64, 128 × 128, multiscale CNN consists of 9 layers, including input layer, 3 convolution layers, 3 sampling layers, full connection layer, and output layer. Continue to segment the gray image of humidity component, and calculate the mean value KT3 of KT3 to construct the landscape probability map based on KT3, as shown in the following equations: KT3j. If |Pwj etness ∗ Wj − Pwmeetanness| > 3 ∗ Pwstdetness, the spot is removed and the error is calculated repeatedly until the result converges

Quantization Effect Analysis of Deep Convolutional Neural Network

Findings

Discussion

Conclusion