What is it about?
Generative AI (GenAI) offers opportunities to synthesize image data with variations beyond what was previously possible. Because large public datasets of militairy reconnaissance are scarse, this would allow to build a large training set with synthetic data. Yet, off-the-shelf GenAI models lack knowledge of the militairy domain, so the generated training examples are not that relevant for real-world scenarios. A large number of state-of-the-art GenAI techniques are discussed. The militairy domain provides an additional challenge, because several sensor modalities have to be combined (visible light, infrared, radar, sonar) and the modifications on the standard militairy vehicles change their silhouette and appareance
Featured Image
Photo by Alexandre Daoust on Unsplash
Why is it important?
Decisions on the modern battlefield are now driven on the scene understanding provided by combining the observations of a diversity of sensors. Mistakes in the real-time understanding of the scenes can be deadly, both for the involved combatants and the remaining civilians.
Perspectives
Overview papers like this one are essential when you start to work on an application in this field. For many domains multiple surveys are written, this is really the first one for GenAI in militairy AI.
Dr. Arnoud Visser
Universiteit van Amsterdam
Read the Original
This page is a summary of: Generative AI methods for synthesis of image data to train AI for automated scene understanding in a military context: a review of opportunities, May 2025, SPIE,
DOI: 10.1117/12.3053494.
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