Urban Structure Types (USTs) stand for areas with homogeneous appearance over the urban matrix. The use of spatial metrics rises as a convenient alternative to quantify the homogeneity of areas on a specific scale. Remote sensing imagery is largely used to assess and study the urban environment, and its classification is a way to recreate the Earth’s surface digitally, both natural and urban spaces. This study proposes a method for city-scale UST mapping using remote sensing images as the unique source of information. Such a proposal comprehends the classification of images that express spatial metrics derived from previous land use and land cover (LULC) classification. We carried two case studies to assess the proposed method under different image resolutions and urban complexity conditions. For this purpose, Landsat-8 OLI and Sentinel-2 MSI images acquired from different cities in Brazil are submitted to the proposed method. An alternative object-based image classification method is included as a comparison baseline. The proposed method shows efficiency in the UST mapping process, which is highly influenced by the neighborhood size considered over the process. Also, it is verified that the proposed method is superior at a significance level of 5%.

城市结构类型 (UST) 代表在城市矩阵上具有均匀外观的区域。空间度量的使用成为量化特定尺度区域同质性的便捷替代方法。遥感影像主要用于评估和研究城市环境，其分类是一种以数字方式重建地球表面的方法，包括自然空间和城市空间。本研究提出了一种使用遥感图像作为唯一信息源的城市尺度 UST 制图方法。这样的提议包含表达从先前土地利用和土地覆盖 (LULC) 分类得出的空间度量的图像分类。我们进行了两个案例研究，以在不同的图像分辨率和城市复杂性条件下评估所提出的方法。以此目的，从巴西不同城市获取的 Landsat-8 OLI 和 Sentinel-2 MSI 图像提交给所提出的方法。包含替代的基于对象的图像分类方法作为比较基线。所提出的方法显示出 UST 映射过程的效率，这在很大程度上受过程中考虑的邻域大小的影响。此外，验证了所提出的方法在 5% 的显着性水平上是优越的。