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A convolutional neural network approach to predict non-permissive environments from moderate-resolution imagery

机译：一种卷积神经网络方法，以预测中频分辨率图像的非允许环境

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Convolutional neural networks (CNNs) trained with satellite imagery have been successfully used to generate measures of development indicators, such as poverty, in developing nations. This article explores a CNN-based approach leveraging Landsat 8 imagery to predict locations of conflict-related deaths. Using Nigeria as a case study, we use the Armed Conflict Location & Event Data (ACLED) dataset to identify locations of conflict events that did or did not result in a death. Imagery for each location is used as an input to train a CNN to distinguish fatal from non-fatal events. Using 2014 imagery, we are able to predict the result of conflict events in the following year (2015) with 80% accuracy. While our approach does not replace the need for causal studies into the drivers of conflict death, it provides a low-cost solution to prediction that requires only publicly available imagery to implement. Findings suggest that the information contained in moderate-resolution imagery can be used to predict the likelihood of a death due to conflict at a given location in Nigeria the following year, and that CNN-based methods of estimating development-related indicators may be effective in applications beyond those explored in the literature.
机译：利用卫星图像训练的卷积神经网络（CNN）已成功用于生成发展中国家的发展指标，如贫困指标。本文探讨了一种基于CNN的方法，利用陆地卫星8号图像预测与冲突有关的死亡地点。以尼日利亚为例，我们使用武装冲突地点和事件数据（ACLED）数据集来确定导致或未导致死亡的冲突事件的地点。每个位置的图像被用作输入，以训练CNN区分致命事件和非致命事件。利用2014年的图像，我们能够以80%的准确率预测下一年（2015年）冲突事件的结果。虽然我们的方法不能取代对冲突死亡驱动因素的因果研究，但它提供了一种低成本的预测解决方案，只需要公开可用的图像即可实现。研究结果表明，中分辨率图像中包含的信息可用于预测下一年在尼日利亚某一特定地点因冲突而死亡的可能性，基于CNN的估计发展相关指标的方法在文献中探讨的应用之外可能是有效的。

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