Spatiotemporal Prediction of Conflict Fatality Risk Using Convolutional Neural Networks and Satellite Imagery
As both satellite imagery and image-based machine learning methods continue to improve and become more accessible, they are being utilized in an increasing number of sectors and applications. Recent applications using convolutional neural networks (CNNs) and satellite imagery include estimating soci...
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MDPI AG
2024-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/18/3411 |
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| author | Seth Goodman Ariel BenYishay Daniel Runfola |
| author_facet | Seth Goodman Ariel BenYishay Daniel Runfola |
| author_sort | Seth Goodman |
| collection | DOAJ |
| description | As both satellite imagery and image-based machine learning methods continue to improve and become more accessible, they are being utilized in an increasing number of sectors and applications. Recent applications using convolutional neural networks (CNNs) and satellite imagery include estimating socioeconomic and development indicators such as poverty, road quality, and conflict. This article builds on existing work leveraging satellite imagery and machine learning for estimation or prediction, to explore the potential to extend these methods temporally. Using Landsat 8 imagery and data from the Armed Conflict Location & Event Data Project (ACLED) we produce subnational predictions of the risk of conflict fatalities in Nigeria during 2015, 2017, and 2019 using distinct models trained on both yearly and six-month windows of data from the preceding year. We find that predictions at conflict sites leveraging imagery from the preceding year for training can predict conflict fatalities in the following year with an area under the receiver operating characteristic curve (AUC) of over 75% on average. While models consistently outperform a baseline comparison, and performance in individual periods can be strong (AUC > 80%), changes based on ground conditions such as the geographic scope of conflict can degrade performance in subsequent periods. In addition, we find that training models using an entire year of data slightly outperform models using only six months of data. Overall, the findings suggest CNN-based methods are moderately effective at detecting features in Landsat satellite imagery associated with the risk of fatalities from conflict events across time periods. |
| format | Article |
| id | doaj-art-38f33b5cc84344629291e8751cc7de8a |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-38f33b5cc84344629291e8751cc7de8a2025-08-20T01:55:49ZengMDPI AGRemote Sensing2072-42922024-09-011618341110.3390/rs16183411Spatiotemporal Prediction of Conflict Fatality Risk Using Convolutional Neural Networks and Satellite ImagerySeth Goodman0Ariel BenYishay1Daniel Runfola2AidData, Global Research Institute, William & Mary, Williamsburg, VA 23185, USAAidData, Global Research Institute, William & Mary, Williamsburg, VA 23185, USADepartment of Applied Science, William & Mary, Williamsburg, VA 23185, USAAs both satellite imagery and image-based machine learning methods continue to improve and become more accessible, they are being utilized in an increasing number of sectors and applications. Recent applications using convolutional neural networks (CNNs) and satellite imagery include estimating socioeconomic and development indicators such as poverty, road quality, and conflict. This article builds on existing work leveraging satellite imagery and machine learning for estimation or prediction, to explore the potential to extend these methods temporally. Using Landsat 8 imagery and data from the Armed Conflict Location & Event Data Project (ACLED) we produce subnational predictions of the risk of conflict fatalities in Nigeria during 2015, 2017, and 2019 using distinct models trained on both yearly and six-month windows of data from the preceding year. We find that predictions at conflict sites leveraging imagery from the preceding year for training can predict conflict fatalities in the following year with an area under the receiver operating characteristic curve (AUC) of over 75% on average. While models consistently outperform a baseline comparison, and performance in individual periods can be strong (AUC > 80%), changes based on ground conditions such as the geographic scope of conflict can degrade performance in subsequent periods. In addition, we find that training models using an entire year of data slightly outperform models using only six months of data. Overall, the findings suggest CNN-based methods are moderately effective at detecting features in Landsat satellite imagery associated with the risk of fatalities from conflict events across time periods.https://www.mdpi.com/2072-4292/16/18/3411machine learningconvolutional neural networksGeoAILandsatsatellite imageryconflict |
| spellingShingle | Seth Goodman Ariel BenYishay Daniel Runfola Spatiotemporal Prediction of Conflict Fatality Risk Using Convolutional Neural Networks and Satellite Imagery Remote Sensing machine learning convolutional neural networks GeoAI Landsat satellite imagery conflict |
| title | Spatiotemporal Prediction of Conflict Fatality Risk Using Convolutional Neural Networks and Satellite Imagery |
| title_full | Spatiotemporal Prediction of Conflict Fatality Risk Using Convolutional Neural Networks and Satellite Imagery |
| title_fullStr | Spatiotemporal Prediction of Conflict Fatality Risk Using Convolutional Neural Networks and Satellite Imagery |
| title_full_unstemmed | Spatiotemporal Prediction of Conflict Fatality Risk Using Convolutional Neural Networks and Satellite Imagery |
| title_short | Spatiotemporal Prediction of Conflict Fatality Risk Using Convolutional Neural Networks and Satellite Imagery |
| title_sort | spatiotemporal prediction of conflict fatality risk using convolutional neural networks and satellite imagery |
| topic | machine learning convolutional neural networks GeoAI Landsat satellite imagery conflict |
| url | https://www.mdpi.com/2072-4292/16/18/3411 |
| work_keys_str_mv | AT sethgoodman spatiotemporalpredictionofconflictfatalityriskusingconvolutionalneuralnetworksandsatelliteimagery AT arielbenyishay spatiotemporalpredictionofconflictfatalityriskusingconvolutionalneuralnetworksandsatelliteimagery AT danielrunfola spatiotemporalpredictionofconflictfatalityriskusingconvolutionalneuralnetworksandsatelliteimagery |