In the second of two guest posts for OJB, first published on the ML Satellites blog, MA Data Journalism student Federico Acosta Rainis uses the 8 angles used by data journalists framework to explore satellite image-driven journalism.

Satellite-driven stories don’t have to use using artificial intelligence (AI) — many can be told using satellite data alone, without. The main advantages of AI include quantifying phenomena, identifying patterns, showing changes or finding a “needle in a haystack” across large territories or different time periods.

AI algorithms can also be used to automate a process: since satellites produce recurring data, you can build, for example, a platform that automatically detects changes in the size of forests.

Paul Bradshaw’s framework for data journalism angles recognises eight types of stories: scale, change, ranking, variation, exploration, exploration, relationships, stories about data and stories through data. The same framework can be adopted to generate ideas for satellite journalism, too.

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In the first of two guest posts for OJB, first published on ML Satellites, MA Data Journalism student Federico Acosta Rainis explains how to get started with satellite journalism — and avoid common pitfalls.

Working with satellite imagery and AI models takes time and patience. There is no general rule: you have to find the right model for each case, in a process of trial and error, while crunching large amounts of data.

That is why the advice of Anatoly Bondarenko, data editor of Texty, is crucial:

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