Remote sensing techniques are now commonly applied to map and monitor urban land uses to measure growth and to assist with development and planning. Recent work in this area has highlighted the use of textures and other spatial features that can be measured in very high spatial resolution imagery. Far less attention has been given to using geospatial vector data (i.e. points, lines, polygons) to map land uses. This paper presents an approach to distinguish residential settlement types (regular vs. irregular) using an existing database of settlement points locating structures. Nine data features describing the density, distance, angles, and spacing of the settlement points are calculated at multiple spatial scales. These data are analysed alone and with five common remote sensing measures on elevation, slope, vegetation, and nighttime lights in a supervised machine learning approach to classify land use areas. The method was tested in seven provinces of Afghanistan (Balkh, Helmand, Herat, Kabul, Kandahar, Kunduz, Nangarhar). Overall accuracy ranged from 78% in Kandahar to 90% in Nangarhar. This research demonstrates the potential to accurately map land uses from even the simplest representation of structures.

中文翻译：

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通过机器学习方法从定居点识别住宅小区类型

遥感技术现在普遍应用于绘制和监测城市土地利用情况，以衡量增长并协助开发和规划。该领域最近的工作强调了纹理和其他空间特征的使用，这些特征可以在非常高的空间分辨率图像中进行测量。使用地理空间矢量数据（即点、线、多边形）绘制土地利用地图的关注度要低得多。本文提出了一种使用现有的聚落点定位结构数据库来区分住宅聚落类型（规则与不规则）的方法。在多个空间尺度上计算描述聚落点的密度、距离、角度和间距的九个数据特征。这些数据单独进行分析，并通过监督机器学习方法对海拔、坡度、植被和夜间灯光进行五种常见的遥感测量，以对土地利用区域进行分类。该方法在阿富汗七个省（巴尔赫、赫尔曼德、赫拉特、喀布尔、坎大哈、昆都士、楠格哈尔）进行了测试。总体准确率从坎大哈的 78% 到楠格哈尔的 90% 不等。这项研究表明，即使是最简单的结构表示，也有可能准确绘制土地利用地图。