Russian DataVision unveils neural video compression for low-bandwidth drone control
Bottom line
Russian firm DataVision has unveiled a ~50g software-hardware module that uses onboard neural networks to compress drone video feeds to 25-70 kbps, enabling object recognition and control over poor connectivity links.
Why it matters
This technology could significantly enhance the resilience of Russian drone swarms and FPV operators against electronic warfare (EW) jamming, allowing for more persistent ISR and strike capabilities in GPS-denied or EW-saturated environments.
Pictures from the cited posts

photo Беспилот (Bespilot — UAV, drones, robots)B 
chart Беспилот (Bespilot — UAV, drones, robots)B 
screenshot Беспилот (Bespilot — UAV, drones, robots)B
Evidence — 1 independent origin
Original text (excerpt)
Видео с дрона при связи в 25 Кбит/с: российская DataVision научила БПЛА передавать картинку по слабому каналу ▶️ Российская компания DataVision создала решение, которое подстраивает объём передаваемых данных под возможности канала и позволяет управлять БПЛА при слабой связи. ПО м
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← Latest findings · All findings from October 2026
History · version 1 · first published · last substantive update
Cite this finding
Robot War (2026). Russian DataVision unveils neural video compression for low-bandwidth drone control. Hetzner OSINT, 22 October 2026. https://robotwar.io/osint-hetzner/2026-10-22--russian-datavision-unveils-neural-video-compression-for-low-bandwidth/
@misc{robotwar-2026-10-22--russian-datavision-unveils-neural-video-compression-for-low-bandwidth,
title = {Russian DataVision unveils neural video compression for low-bandwidth drone control},
author = {Robot War},
howpublished = {\url{https://robotwar.io/osint-hetzner/2026-10-22--russian-datavision-unveils-neural-video-compression-for-low-bandwidth/}},
year = {2026},
note = {Hetzner OSINT finding B3}
}Grades: source reliability A–F from the channel register; information credibility 1–6 from independent origins — reposts count once. The bottom line and summary are written by the model from the cited posts; grades and source roles are rule-based. Methodology 2026-10-18.v7 · Corrections · JSON · RSS · API performance