methodology in which the relevant
components of the search process are
decoupled in a manner akin to our volunteer search, but more patiently architected, evolved, and integrated. For
example, Coast Guard imagery experts
need not be available to board search
planes nationwide; instead, a remote
image-analysis team could examine
streaming (and archived) footage from
multiple planes in different locales.
Weather hazards and other issues suggest removing people boarding planes
entirely; imagery could be acquired
via satellites and unmanned aerial vehicles, which are constantly improving. Furthermore, a component-based
approach takes advantage of the independent evolution of technologies
and the ability to quickly train domain
experts on each component. Image-analysis tools can improve separately
from imaging equipment, which can
evolve separately from devices flying
the equipment. The networking of
components and expertise is becoming relatively common in military settings and public-sector medical imaging. It would be useful to explore these
ideas further for civilian settings like
SAR, especially in light of their potential application to adjacent topics like
disaster response.
Automated image analysis. The volunteer search team included experts
in image processing in astronomy, as
well as in computer vision. The consensus early on was that off-the-shelf
image-recognition software wouldn’t
be accurate enough for the urgent task
of identifying boats in satellite imagery of open ocean. During the course
of the search a number of machine-vision experts examined the available
data sets, concluding they were not
of sufficient quality for automated
processing, though it may have been
because we lacked access to the “raw
bits” obtained by satellite-based sensors. Though some experts attempted
a simple form of automated screening by looking for clusters of adjacent
pixels that stood out from the background, even these efforts were relatively unsuccessful.
It would be good to know if the
problem of finding small boats in sat-
ellite imagery of the ocean is inherent-
ly difficult or simply requires more fo-
cused attention from computer-vision
researchers. The problem of using re-
mote imagery for SAR operations is a
topic for which computer vision would
seem to have a lot to offer, especially at
sea, where obstructions are few.
Reflection
Having described the amateur SAR
processes cobbled together to find
Tenacious, we return to some of the issues we outlined initially when we met
in Berkeley in 2008.
On the computational front, there
are encouraging signs that SAR can
be “democratized” to the point where
a similar search could be conducted
without extraordinary access to expertise and resources. The price of
computer hardware has continued to
shrink, and cloud services are com-moditizing access to large computational clusters; it is now affordable to
get quick access to enormous computing resources without social connections or up-front costs. In contrast,
custom software pipelines for tasks
like image processing, drift modeling,
and command-and-control coordination are not widely available. This software vacuum is not an inherent problem but is an area where small teams
of open-source developers and software researchers could have significant impact. The key barrier to SAR democratization may be access to data.
Not clear is whether data providers
(such as those in satellite imagery and
in plane leasing) would be able to support large-scale, near-real-time feeds
of public-safety-related imagery. Also
not clear, from a policy perspective,
is whether such a service is an agreed-upon social good. This topic deserves
more public discussion and technical
investigation. Sometimes the best way
to democratize access to resources is
to build disruptive low-fidelity prototypes; perhaps then this discussion
can be accelerated through low-fidelity
open-source prototypes that make the
best of publicly available data (such as
by aggregating multiple volunteer We-bcams3).
The volunteer search team’s experi-
ence reinforces the need for technical
advances in social computing. In the
end, the team exploited technology for
many uses, not just the high-profile
task of locating Tenacious in images
from space. Modern networked tech-
nologies enabled a group of acquain-
tances and strangers to quickly self-
organize, coordinate, build complex
working systems, and attack problems
in a data-driven manner. Still, the pro-
cess of coordinating diverse volunteer
skills in an emerging crisis was quite
difficult, and there is significant room
for improvement over standard email
and blogging tools. A major challenge
is to deliver solutions that exploit the
software that people already use in
their daily lives.
References
1. goldstein, J. and rotich, J. Digitally Networked
Technology in Kenya’s 2007–2008 Post-Election
Crisis. Technical Report 2008–2009. Berkman center
for Internet and society at harvard university,
cambridge, Ma, sept. 2008.
2. heinzelman, J. and Waters, c. Crowdsourcing Crisis
Information in Disaster-Affected Haiti. Technical
Report, Special Report 252. united states Institute of
Peace, Washington, D.c., oct. 2010.
3. hellerstein, J. M. and tennenhouse, D.l. Searching
for Jim Gray: A Technical Overview, Technical Report
UCB/EECS-2010-142. eecs Department, university of
california, Berkeley, Dec. 2010.
4. saade, e. search survey for s/V Tenacious: gulf of
farallones and approaches to san francisco Bay. ACM
SIGMOD Record 37, 2 (June 2008), 70–77.
5. u.s. coast guard. Search and Rescue Optimal
Planning System (SAROPS) 2009; http://www.uscg.
mil/acquisition/international/ sarops.asp
Joseph M. hellerstein ( hellerstein@berkeley.edu) is a
professor in the eecs computer science Division of the
university of california, Berkeley.
David L. Tennenhouse ( dtennenhouse@nvpllc.com) is a
partner in new Venture Partners, a venture-capital firm
with offices in california, new Jersey, and the u.k., and
former head of research at Intel.
© 2011 acM 0001-0782/11/07 $10.00