Geospatial Intelligence and Artificial Intelligence for Detecting Potential Coca Paste Production Infrastructure in the Border Region of Venezuela and Colombia

IF 1.1 Q3 CRIMINOLOGY & PENOLOGY
Jairo Jesús Pinto Hidalgo, Jorge Antonio Silva Centeno
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引用次数: 2

Abstract

Abstract Cocaine production has reached record levels in recent years. Latin America and the Caribbean are the primary sources of all cocaine consumed globally, thus there are indications that cocaine production processes could spread to countries of transit and consumption, becoming a threat to the security of states. In this article, we address the challenge of detecting potential primary infrastructures to produce coca paste in the border region of Venezuela and Colombia. We use geospatial intelligence and artificial intelligence to detect these objects in remote sensing images and identify their geographic location. We generated a dataset of 16,778 training samples that we named CocaPaste-PI-DETECTION, constructed from PlanetScope satellite imagery rated at NIIRS level 3, ground truth data, and A1, A2, and B2 information sources. An advanced deep learning model, specialized for object detection tasks, was trained. A mean Average Precision (mAP) score of 90.07% was obtained, and we analyzed generalization capabilities and conducted different experiments that demonstrated how the proposed methodology could strengthen intervention strategies against drug trafficking.
用于探测委内瑞拉和哥伦比亚边境地区潜在古柯膏生产基础设施的地理空间智能和人工智能
近年来,可卡因的产量达到了创纪录的水平。拉丁美洲和加勒比是全球消费的所有可卡因的主要来源,因此有迹象表明,可卡因的生产过程可能蔓延到过境国和消费国,对各国的安全构成威胁。在这篇文章中,我们解决了在委内瑞拉和哥伦比亚边境地区发现潜在的生产古柯膏的初级基础设施的挑战。我们利用地理空间智能和人工智能在遥感图像中检测这些物体并确定其地理位置。我们生成了一个包含16,778个训练样本的数据集,我们将其命名为cocapast - pi - detection,该数据集由NIIRS 3级的PlanetScope卫星图像、地面真实数据以及A1、A2和B2信息源构建而成。训练了一个专门用于目标检测任务的高级深度学习模型。平均精度(mAP)得分为90.07%,我们分析了该方法的泛化能力,并进行了不同的实验,以证明该方法可以加强对毒品贩运的干预策略。
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来源期刊
Journal of Applied Security Research
Journal of Applied Security Research CRIMINOLOGY & PENOLOGY-
CiteScore
2.90
自引率
15.40%
发文量
35
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