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.

中文翻译：

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基于多尺度卷积神经网络的植物与现代城市景观空间尺度定量评价

现代城市景观是一个简单的生态系统，对城市的可持续发展具有重要意义。本研究提出一种基于深度卷积神经网络的景观信息提取模型，研究多尺度景观卷积神经网络分类方法，构建基于多尺度CNN的景观信息提取模型，最后分析深度卷积神经网络的定量效果。结果表明，通过计算混淆矩阵、生产精度和用户精度，总体kappa系数为0.91，分类精度为93%。本研究提出的方法可以识别90%以上的水目标，用户准确率和生产准确率分别为99.78%和91.94%，总体准确率为93.33%。本研究提出的方法明显优于其他方法，kappa系数和整体精度都是最好的。本研究为现代城市景观空间尺度的定量评价提供了一定的参考价值。