Satellite imagery is increasingly a key asset for journalists. Looking from above often allows us to put a story into context, take a more interesting perspective or show what some power prefers to keep hidden.
But with hundreds of satellites taking thousands of images of the Earth every day, it is difficult to separate the wheat from the chaff. How can we find relevant stories in this ocean of data?
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.
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: