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Computer vision model detecting grain-storage facilities in an aerial photo

Neural networks are helping humanitarian observers measure the extent of war damage to Ukraine’s grain crop.

What’s new: Analysts from the Yale School of Public Health and Oak Ridge National Laboratory built a computer vision model that detects grain-storage facilities in aerial photos. Its output helped them identify facilities damaged by the Russian invasion.

How it works: The authors started with a database of grain silos last updated in 2019. They used machine learning to find facilities missing from that survey or built since then.

  • The authors used a YOLOv5 object detector/classifier that Yale researchers previously had trained to identify crop silos in images from Google Earth. They fine-tuned the model to identify other types of facilities — grain elevators, warehouses, and the like — in labeled images from commercial satellites.
  • In tests, the model achieved 83.6 percent precision and 73.9 percent recall.
  • They fed the model 1,787 satellite images of areas in Ukraine that were affected by the conflict, dated after February 24 (the start of the current Russian invasion). The model identified 19 previously uncatalogued crop facilities.
  • Having located the grain facilities, the authors evaluated damage manually.

Results: Among 344 facilities, they found that 75 had suffered damage. They estimate that the destruction has compromised 3.07 million tons of grain storage capacity, nearly 15 percent of Ukraine’s total.

Why it matters: Before the war, Ukraine was the world’s fifth-largest wheat exporter. By disrupting this activity, the Russian invasion has contributed to a spike in global food prices, which observers warn may lead to famine. Understanding the scope of the damage to Ukraine’s grain supply could help leaders estimate shortfalls and plan responses.

Behind the news: Machine learning has been applied to a variety of information in the war between Russia and Ukraine. It has been used to verify the identities of prisoners of war, noncombatants fleeing conflict zones, and soldiers accused of committing war crimes. It has also been used to debunk propaganda, monitor the flow of displaced persons, and locate potentially damaged buildings obscured by smoke and clouds.

We’re thinking: War is terrible. We’re glad that AI can help document the damage caused by invading forces, and we hope that such documentation will lead to payment of appropriate reparations.


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