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.