Social instability and logistical factors like the displacement of vulnerable populations, the difficulty of accessing these populations, and the lack of geographic information for hard-to-reach areas continue to serve as barriers to global essential immunizations (EI). Microplanning, a population-based, healthcare intervention planning method has begun to leverage geographic information system (GIS) technology and geospatial methods to improve the remote identification and mapping of vulnerable populations to ensure inclusion in outreach and immunization services, when feasible. We compare two methods of accomplishing a remote inventory of building locations to assess their accuracy and similarity to currently employed microplan line-lists in the study area. The outputs of a crowd-sourced digitization effort, or mapathon, were compared to those of a machine-learning algorithm for digitization, referred to as automatic feature extraction (AFE). The following accuracy assessments were employed to determine the performance of each feature generation method: (1) an agreement analysis of the two methods assessed the occurrence of matches across the two outputs, where agreements were labeled as “befriended” and disagreements as “lonely”; (2) true and false positive percentages of each method were calculated in comparison to satellite imagery; (3) counts of features generated from both the mapathon and AFE were statistically compared to the number of features listed in the microplan line-list for the study area; and (4) population estimates for both feature generation method were determined for every structure identified assuming a total of three households per compound, with each household averaging two adults and 5 children. The mapathon and AFE outputs detected 92,713 and 53,150 features, respectively. A higher proportion (30%) of AFE features were befriended compared with befriended mapathon points (28%). The AFE had a higher true positive rate (90.5%) of identifying structures than the mapathon (84.5%). The difference in the average number of features identified per area between the microplan and mapathon points was larger (t = 3.56) than the microplan and AFE (t = − 2.09) (alpha = 0.05). Our findings indicate AFE outputs had higher agreement (i.e., befriended), slightly higher likelihood of correctly identifying a structure, and were more similar to the local microplan line-lists than the mapathon outputs. These findings suggest AFE may be more accurate for identifying structures in high-resolution satellite imagery than mapathons. However, they both had their advantages and the ideal method would utilize both methods in tandem.

中文翻译：

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Mapathons 与自动特征提取：加强免疫微规划的比较分析

社会不稳定和后勤因素，如弱势群体的流离失所、接触这些群体的困难以及难以到达的地区缺乏地理信息，继续成为全球基本免疫接种 (EI) 的障碍。微观规划是一种基于人群的医疗干预规划方法，已开始利用地理信息系统 (GIS) 技术和地理空间方法来改进对弱势人群的远程识别和测绘，以确保在可行的情况下纳入外展和免疫服务。我们比较了两种完成建筑位置远程清点的方法，以评估它们与研究区域中当前采用的微型计划线列表的准确性和相似性。众包数字化工作或地图马拉松的输出，与用于数字化的机器学习算法进行比较，称为自动特征提取（AFE）。以下准确性评估用于确定每种特征生成方法的性能：（1）两种方法的一致性分析评估了两个输出之间匹配的发生，其中协议被标记为“友好”，而分歧被标记为“孤独” ; (2) 与卫星图像相比，计算了每种方法的真假阳性百分比；(3) 将 mapathon 和 AFE 生成的特征计数与研究区域的微平面线列表中列出的特征数量进行统计比较；(4) 两种特征生成方法的人口估计值是为每个确定的结构确定的，假设每个化合物共有三个家庭，每个家庭平均有两个成人和 5 个孩子。mapathon 和 AFE 输出分别检测到 92,713 和 53,150 个特征。与成为好友的地图马拉松点 (28%) 相比，更高比例 (30%) 的 AFE 特征成为好友。AFE 识别结构的真阳性率 (90.5%) 高于 mapathon (84.5%)。微计划和地图马拉松点之间每个区域识别的平均特征数量的差异（t = 3.56）大于微计划和 AFE（t = - 2.09）（α = 0.05）。我们的研究结果表明 AFE 输出具有更高的一致性（即，成为朋友），正确识别结构的可能性略高，并且与 mapathon 输出相比，与局部微计划线列表更相似。这些发现表明 AFE 在识别高分辨率卫星图像中的结构方面可能比地图马拉松更准确。然而，它们都有各自的优点，理想的方法是同时使用这两种方法。