Public vs. Private Financing of Remotely Sensed Imagery Data - The Need for Both
Ed. Note: This brief article, which originally
appeared in The Quarterly Newsletter of the IEEE-GRSS Private
Sector Liaison Group's Dec. 19 issue, is reprinted with permission.
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.