Accurate mapping of slums is crucial for urban planning and management. This article proposes a machine learning, hierarchical object-based method to map slum settlements using very high-resolution (VHR) imagery and land boundary data to support slum upgrading. The proposed method is tested in Kingston Metropolitan Area, Jamaica. First, the VHR imagery is classified into major land cover classes (i.e., the initial land cover map). Second, the VHR imagery and land boundary layer are used to obtain homogenous neighborhoods (HNs). Third, the initial land cover map is used to derive multiple context, spectral, and texture image features according to the local physical characteristics of slum settlements. Fourth, a machine-learning classifier, classification and regression trees, is used to classify HNs into slum and nonslum settlements using only the effective image features. Finally, reference data collected manually are used to assess the accuracy of the classification. In the training site, an overall accuracy of 0.935 is achieved. The effective image indicators for slum mapping include the building layout, building density, building roof characteristics, and distance from buildings to gullies. The classifier and those features selected from the training site are further used to map slums in two validating sites to assess the transferability of our approach. Overall accuracy of the two validating sites reached 0.928 and 0.929, respectively, suggesting that the features and classification model obtained from one site has the potential to be transferred to other areas in Jamaica and possibly other developing Caribbean countries with similar situation and data availability.

中文翻译：

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使用高分辨率影像和土地边界数据绘制城市贫民窟定居点地图

准确绘制贫民窟地图对于城市规划和管理至关重要。本文提出了一种机器学习、基于分层对象的方法，使用超高分辨率 (VHR) 图像和土地边界数据来绘制贫民窟定居点地图，以支持贫民窟升级。所提出的方法在牙买加金斯敦都市区进行了测试。首先，VHR 图像被分为主要的土地覆盖类别（即初始土地覆盖地图）。其次，使用 VHR 图像和陆地边界层来获得同质邻域 (HN)。第三，根据贫民窟的局部物理特征，使用初始土地覆盖图导出多个上下文、光谱和纹理图像特征。第四，机器学习分类器，分类和回归树，用于仅使用有效图像特征将 HN 分类为贫民窟和非贫民窟定居点。最后，手动收集的参考数据用于评估分类的准确性。在训练站点中，实现了 0.935 的整体准确度。贫民窟测绘的有效图像指标包括建筑布局、建筑密度、建筑屋顶特征以及建筑到沟壑的距离。分类器和从训练站点中选择的那些特征进一步用于绘制两个验证站点中的贫民窟地图，以评估我们方法的可转移性。两个验证站点的总体准确率分别达到了 0.928 和 0.929，