利用卷积神经网络和卫星图像对冲突死亡风险进行时空预测

IF 4.2 2区 地球科学 Q2 ENVIRONMENTAL SCIENCES
Remote Sensing Pub Date : 2024-09-13 DOI:10.3390/rs16183411
Seth Goodman, Ariel BenYishay, Daniel Runfola
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引用次数: 0

摘要

随着卫星图像和基于图像的机器学习方法的不断改进和普及,它们正被越来越多的领域和应用所使用。最近使用卷积神经网络(CNN)和卫星图像的应用包括估算贫困、道路质量和冲突等社会经济和发展指标。本文以利用卫星图像和机器学习进行估算或预测的现有工作为基础,探索从时间上扩展这些方法的潜力。利用大地遥感卫星 8 号(Landsat 8)图像和武装冲突地点与事件数据项目(ACLED)的数据,我们使用在前一年的年度数据和六个月数据窗口中训练出来的不同模型,对 2015、2017 和 2019 年尼日利亚的冲突死亡风险进行了国家以下级别的预测。我们发现,利用前一年的图像对冲突地点进行预测训练,可以预测下一年的冲突死亡事件,接收器工作特征曲线下的面积(AUC)平均超过 75%。虽然模型的性能始终优于基线比较,而且在个别时期的性能也很强(AUC > 80%),但基于地面条件(如冲突的地理范围)的变化会降低后续时期的性能。此外,我们还发现使用全年数据训练的模型略优于仅使用六个月数据的模型。总之,研究结果表明,基于 CNN 的方法在检测 Landsat 卫星图像中与跨时段冲突事件死亡风险相关的特征方面效果一般。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.
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来源期刊
Remote Sensing
Remote Sensing REMOTE SENSING-
CiteScore
8.30
自引率
24.00%
发文量
5435
审稿时长
20.66 days
期刊介绍: Remote Sensing (ISSN 2072-4292) publishes regular research papers, reviews, letters and communications covering all aspects of the remote sensing process, from instrument design and signal processing to the retrieval of geophysical parameters and their application in geosciences. Our aim is to encourage scientists to publish experimental, theoretical and computational results in as much detail as possible so that results can be easily reproduced. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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