A Convolutional Neural Network Approach To Predict Non-Permissive Environments From Moderate-Resolution Imagery

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.