BACKGROUND This is the third paper in a 3-paper series evaluating alternative models for rapidly estimating neighborhood populations using limited survey data, augmented with aerial imagery. METHODS Bayesian methods were used to sample the large solution space of candidate regression models for estimating population density. RESULTS We accurately estimated the population densities and counts of 20 neighborhoods in the city of Bo, Sierra Leone, using statistical measures derived from Landsat multi-band satellite imagery. The best regression model proposed estimated the latter with an absolute median proportional error of 8.0%, while the total population of the 20 neighborhoods was estimated with an error of less than 1.0%. We also compare our results with those obtained using an empirical Bayes approach. CONCLUSIONS Our approach provides a rapid and effective method for constructing predictive models for population densities and counts utilizing remote sensing imagery. Our results, including cross-validation analysis, suggest that masking non-urban areas in the Landsat section images prior to computing the candidate covariate regressors should further improve model generality.

中文翻译：

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使用Landsat图像估算城市人口规模：以西非塞拉利昂博为例。

背景技术这是3篇论文系列中的第三篇论文，该论文评估了使用有限的调查数据并通过航空影像增强的快速估算邻域人口的替代模型。方法使用贝叶斯方法对候选回归模型的较大解空间进行抽样，以估计人口密度。结果我们使用从Landsat多波段卫星图像获得的统计量，准确估算了塞拉利昂博市20个社区的人口密度和计数。提出的最佳回归模型估计后者的绝对中位数比例误差为8.0％，而估计的20个社区的总人口的误差小于1.0％。我们还将我们的结果与使用经验贝叶斯方法获得的结果进行比较。结论我们的方法提供了一种快速有效的方法，可以利用遥感图像构建人口密度和计数的预测模型。我们的结果（包括交叉验证分析）表明，在计算候选协变量回归变量之前，对Landsat剖面图像中的非城市区域进行遮罩应该可以进一步提高模型的通用性。