The identification of landscape classes facilitates the implementation of planning strategies. Although landscape patterns are key distinctive features of landscape classes, existing unsupervised clustering techniques for clustering landscapes rely on categorical input data to quantify such patterns and consider only a limited number of pattern metrics. To unlock the great potential of continuous spatial data, such as remote sensing images, for generating landscape typologies, we adapted a novel unsupervised deep learning method (Deep Convolutional Embedded Clustering; DCEC) to generate a landscape typology for Switzerland. DCEC encodes lower-dimensional representations of input images in a hidden layer, which is simultaneously used to divide the images into well-distinguishable clusters. We applied DCEC to image tiles extracted from satellite images as well as ecological, demographic and terrain layers. DCEC successfully distinguished 45 landscape classes in the continuous input data. We conclude that DCEC is a promising new method in landscape and land-system research.

景观等级的识别有助于规划策略的实施。尽管景观模式是景观类的关键显着特征，但现有的用于聚类景观的无监督聚类技术依赖于分类输入数据来量化这些模式，并且只考虑有限数量的模式指标。为了释放连续空间数据（例如遥感图像）生成景观类型的巨大潜力，我们采用了一种新颖的无监督深度学习方法（深度卷积嵌入式聚类；DCEC）来为瑞士生成景观类型。DCEC 在隐藏层中对输入图像的低维表示进行编码，同时用于将图像划分为可区分的簇。我们将 DCEC 应用于从卫星图像以及生态、人口和地形图层中提取的图像切片。DCEC 在连续输入数据中成功区分了 45 个景观类别。我们得出结论，DCEC 是景观和土地系统研究中一种很有前途的新方法。