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 ( [e-mail]) is a professor in the eecs computer science Division of the university of california, Berkeley.

David L. Tennenhouse ( [e-mail]) 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.

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