DeepVA's AI improves rbb’s regional coverage

DeepVA Landmark Recognition


Public broadcasting organization



Streamlines processes, increases staff efficiency and delivers faster, more relevant content, enhancing the value of the archive for viewers and staff alike.

Press release

rbb’s collaboration with DeepVA transformed regional broadcasting and archiving through efficient AI utilization.

Increased efficiency in archiving and production enables quick and easy access to content for editors, but also improves the viewer experience in the long term.

rbb embraces the AI revolution: Efficient archiving and processing with DeepVA solutions

Regional coverage is one of the most important tasks of public service broadcasting and an area that no other broadcaster covers to the same extent. In an era of increasing media consumption, this regionalisation requires resources that reduce and automate the effort involved. This begins at the broadcaster with the processing of documents or the technical preparation of content, but is also to be further advanced in post-production. rbb works closely with government institutions, service providers and research organisations to develop innovative solutions for all areas of the broadcaster. The focus is on supporting editorial teams to work more efficiently.

rbb used DeepVA as part of its process optimisation strategy in post-production, editing and archiving. Among other things, the test focused on landmark recognition to assist archive staff with keywording. DeepVA offered a solution that used AI to speed up, simplify and even partially automate the analysis of metadata. This enabled editors to access the material they needed more quickly and efficiently, and reduced repetitive tasks.

With just a few training images, the AI can be taught regional buildings or points of interest; this few-shot learning makes it very easy to get started with AI technology, and can be done entirely in the archive, without additional staff or external service providers, changing the job description of archive staff from archivist to AI manager – an added incentive as other departments within the broadcaster can also use the AI expertise and training data built up in the archive.r

A win-win situation for staff and viewers

The success of rbb Retro shows that broadcasters’ archives still hold many treasures that can be unearthed in this way. Viewers benefit from a more precise and faster selection of content, which improves the viewing experience and strengthens loyalty to the broadcaster.

For historical archives in particular, AI-powered landmark recognition can help catalogue and preserve important historical moments. This is not only beneficial for more targeted use, but also for educational purposes and the preservation of cultural heritage, which is also an aspect of public broadcasting.

The main objectives of the project were to seamlessly integrate the AI technology into existing workflows and applications, to achieve fast analysis times and high quality metadata. Employees should be able to benefit from AI-based analysis without having to use additional tools. rbb plans to further integrate AI solutions into existing tools and systems to minimise disruption to workflows. Saving resources and speeding up processes are key objectives. In the future, the recognition of objects and terms in image and video material will also be promoted.

In summary, AI-based landmark recognition in a public broadcaster’s archives creates a win-win situation. Staff can work faster and more efficiently, and viewers get better and more relevant content from their region. This shows how technology can help to increase the value and attractiveness of archives for all stakeholders.

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