A short, one sentence description of our project / idea.
Overview News provides simple access to satellite imagery for journalists.
8 words or less, with a verb, target and an outcome that can be measured.
Our mission is to get environmental data used by many more people.
What we will do to make this happen.
We will achieve our mission by identifying and licensing satellite imagery that journalists will actually use in their reporting.
What we will measure to prove it works.
Hundreds of millions of people read stories supported by Earth imagery, and hundreds of outlets regularly rely on our content.
Is this enough?
Economists that specifically study transactions with environmental externalities have proven, both in theory and in practice, the value of open information toward a more equitable and efficient allocation of natural resources. People have won Nobel Prizes for this sort of thing. The decision contexts are disparate, and the solutions therefore don’t easily scale. However, open information is a common, necessary condition across all of the disparate contexts. And it scales. Open information is not sufficient for saving the world; but it’s a good, broad, and important start.
As Kevin Starr says, impact is "not what happens between the ears," but rather "action in the real world." We agree. Ultimately it is about changing behavior. Still, a necessary component of change is that people are well-informed about environmental change and motivated to do something about it. Information is the common component across the custom contexts
| Behavior | Intervention |
|---|---|
| Journalists search for imagery. | Marketing through social media. A lot of imagery is produced and tagged. |
| Journalists purchase imagery. | License and payment systems that are already familiar to journalists. |
| Readers engage with the story. | Visually appealing, well-designed, and interactive. Two-source authentication for trust. |
| Outcome | Normal people consume imagery. |
What is the mechanism we can create and scale up?
We are a revenue-generating, nonprofit organization. We create imagery across different story contexts through deep learning, computer vision, and natural language processing. Machine learning. Our model is rooted in teaching the machine to find gold in the exabytes of data from satellite imagery providers.
Our chosen structure as a 501(c)(3) nonprofit is mainly to do with the capital required to build the requisite technology. This is highly sophisticated matching technology, which means we are squarely in competition with Facebook and Twitter for talent -- which is expensive. However, our returns are not purely financial; most returns are environmental or social benefits of open information. We are not an attractive early stage investment for venture capitalists; but we are an exceedingly good investment for venture philantropists.
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Doer: (Who will do the work?) We will do the work, as an organization. We will extract, organize, and process imagery that reporters will want to use in their reporting.
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Payer: (Who will pay for it to happen?) Reporters at large and small news outlets.
- Is it relevant enough? Yes. There are thousands of stories each week that can benefit from independent information on landscape level change.
- Is it simple enough? Yes. The images can are distributed through third-party stock photography platforms (e.g., Getty News, Shutterstock) that reporters are already accustomed to using for imagery to support their stories.
- Is it cheap enough? Yes. The imagery is effectively the stuff left on the cutting room floor for defense contractors like Planet Labs. Our solution does not cannibalize revenue from the Department of Defense; and we can charge much less than current image access plans.
Our outcome was chosen because it is scalable. It does not directly change a decision. But the meaningful consumption and use of environmental information underlies basically all conservation decisions. We create the fertile ground for decision support tools to effectuate changes in behavior. This generic step -- open information -- is scalable. It's one of the very few globally scalable solutions to meaningfully distribute environmental data.