整合地球观测IMINT与OSINT数据以创造附加值的多源情报信息:以乌克兰-俄罗斯战争为例

Ioannis Kotaridis, Georgios Benekos
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引用次数: 0

摘要

2022年2月24日,俄罗斯入侵乌克兰,预示着一个新的“社交媒体战争”时代的到来。这种“混合战争”超出了军事领域,还包括网络空间的攻击和旨在破坏政府稳定的假新闻。本文的目标是利用地理数据集和最先进的方法,提出一种基于图像智能(IMINT)和地理空间智能(GEOINT)的高级架构。与情报信息(如开源情报[OSINT])的集成为安全和防务决策最终用户产生多智能知识。结果显示了IMINT、OSINT和GEOINT之间和谐而富有创造性的合作。OSINT数据有助于识别和描述当前的气象条件,有助于提高程序的响应能力。乌克兰上空的天气和浓密云层对光学成像卫星构成挑战,但合成孔径雷达(SAR)传感器卫星可以在夜间工作并克服这一问题。我们进行了OSINT和IMINT分析,在入侵后不久监测了局势。OSINT数据有助于选择适当的感兴趣领域。使用正确的地球观测卫星系统和人工智能/机器学习算法是长期关注许多不同地点,发出异常活动警报,并发现可能发生不一致变化的新地方的最佳方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating Earth observation IMINT with OSINT data to create added-value multisource intelligence information: A case study of the Ukraine–Russia war
The Russian invasion of Ukraine on 24 February 2022 heralded a new “social media war” era. This “hybrid warfare” extends beyond the military landscape and includes attacks in cyberspace and fake news with the aim of destabilising governments. The goal of this paper is to present a high-level of architecture based on imagery intelligence (IMINT) and geospatial intelligence (GEOINT) using geographic datasets and state-of-the-art methods. Integration with intelligence information (like Open-Source Intelligence [OSINT]) produces multiintelligent knowledge for security and defence decision-making end users. The results depict a harmonious and creative collaboration between IMINT, OSINT, and GEOINT. OSINT data helps to identify and describe the meteorological conditions that are present, contributing to the procedure’s responsiveness. Weather and dense cloud cover above Ukraine poses a challenge for optical imaging satellites, but synthetic aperture radar (SAR) sensor satellites can operate at night and overcome the problem. We carried out OSINT and IMINT analysis, monitoring the situation shortly after the invasion. OSINT data helped in the choice of an appropriate area of interest. Using the right Earth observation satellite system and artificial intelligence/machine learning algorithms is the best way to keep an eye on many different sites over long periods, send out alerts about unusual activity, and find new places where incoherent changes might be happening.
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