A convolutional neural network approach to predict non‐permissive environments from moderate‐resolution imagery,Transactions in GIS

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A convolutional neural network approach to predict non‐permissive environments from moderate‐resolution imagery
Transactions in GIS ( IF 2.568 ) Pub Date : 2020-07-13 , DOI: 10.1111/tgis.12661
Seth Goodman _{1,

2} , Ariel BenYishay _{1,

3} , Daniel Runfola _{2,

4}

Affiliation

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.

更新日期：2020-07-13

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