It’s easy to measure return on investment on commercially funded remotely sensed imagery; you can add up the costs and subtract them from the gross revenue of products sold. When such data are publicly funded, the equation is far more complex since the revenue value is rarely measureable in dollars paid, but rather in time saved, animals with a safe environment, floods prevented and the like. Bill Gale explains why both sourcing methods, in balance, are likely to contribute to a sucessful remote sensing future.
Increasingly, remote sensing information is obtained from private sector sources. How is it different from publicly financed remote sensing? One perspective comes from the source of the funding itself. This influences both the purpose and nature of the funded systems. While publicly funded remote sensing systems are intended to further the public interest, privately funded systems aim to make a profit for shareholders through sales of imagery and services to commercial and government customers on a sustained basis.
The success of privately funded systems can be evaluated by spreadsheet, while that of publicly funded systems must somehow be related to diffuse concepts such as the value of a human life or animal habitat, scientific progress, and national prosperity/security. Privately financed ventures commonly undergo a sequence of funding rounds or stages: seed money from individual investors (known as angels, increasingly organized into angel networks - see example of Space Angels Network in "company news" in the newsletter), followed by venture capital through funds that specialize in high-risk early-stage investments, on to mezzanine capital for companies that have proven their business models, and finally stock market funding through an acquisition or initial public offering.
The funding process demands increasing proof of ultimate success; each stage is a test for how well the company has reduced risk and improved its potential to be profitable. Like a funnel, the process ingests many potential good ideas and eliminates all but the best. Could the same model be applied to publicly-funded systems? Organizations such as In-Q-Tel in the intelligence community have indeed attempted to do this. Do public-private partnerships, including those used for remote sensing projects such as NGA's NextView and Germany's TerraSAR, represent the best or the worst of each model? Perhaps we will not know for some time, but achieving the right balance of public and private financing is likely to be a key contributor to remote sensing's success.