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Deep Learning From Multiple Crowds: A Case Study of Humanitarian Mapping

机译：从多个人群中进行深度学习：以人道主义地图绘制为例

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Satellite images are widely applied in humanitarian mapping that labels buildings, roads, and so on for humanitarian aid and economic development. However, the labeling now is mostly done by volunteers. In this paper, we utilize deep learning to solve humanitarian mapping tasks of a mobile software named MapSwipe. The current deep learning techniques, e.g., convolutional neural network (CNN), can recognize ground objects from satellite images but rely on numerous labels for training for each specific task. We solve this problem by fusing multiple freely accessible crowdsourced geographic data and propose an active learning-based CNN training framework named MC-CNN to deal with the quality issues of the labels extracted from these data, including incompleteness (e.g., some kinds of object are not labeled) and heterogeneity (e.g., different spatial granularities). The method is evaluated with building mapping in South Malawi and road mapping in Guinea with level-18 satellite images provided by Bing Map and volunteered geographic information from OpenStreetMap, MapSwipe, and OsmAnd. The results based on multiple metrics, including Precision, Recall, F1 Score, and area under the receiver operating characteristic curve, show that MC-CNN can fuse the crowdsourced labels for higher prediction performance and be successfully applied in MapSwipe for humanitarian mapping with 85% labor saved and an overall accuracy of 0.86 achieved.
机译：卫星图像被广泛应用于人道主义地图，为建筑物，道路等标记人道主义援助和经济发展。但是，现在的标签大部分是由志愿者完成的。在本文中，我们利用深度学习来解决名为MapSwipe的移动软件的人道主义地图绘制任务。当前的深度学习技术，例如卷积神经网络（CNN），可以从卫星图像中识别地面物体，但需要依靠大量标签来训练每个特定任务。我们通过融合多个可自由访问的众包地理数据来解决此问题，并提出了一种基于学习的主动CNN培训框架MC-CNN，以处理从这些数据中提取的标签的质量问题，包括不完整性（例如，某些类型的物体未标记）和异质性（例如，不同的空间粒度）。该方法通过马拉维南部的建筑地图和几内亚的道路地图，必应地图提供的18级卫星图像以及来自OpenStreetMap，MapSwipe和OsmAnd的自愿性地理信息进行评估。基于多个指标的结果，包括精度，召回率，F1得分以及接收器工作特性曲线下的面积，表明MC-CNN可以融合众包标签以实现更高的预测性能，并成功应用于MapSwipe的人道主义地图绘制中（占85％）节省了人工，总体精度达到了0.86。

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