Earth Imaging Journal: Remote Sensing, Satellite Images, Satellite Imagery
Breaking News
SBG Systems to Launch the “SBG +Services”, a Full Set of Technical Services Around its Inertial Sensors
Carrières-sur-Seine, France – SBG Systems, leading manufacturer of inertial...
Foundry Releases Data Fitness Quality-As-A-Service Solution for Geospatial Data Quality and Fitness-For-Use Assessment
(Sun Prairie, WI) — Continental Mapping’s software division –...
Trimble Introduces Lower Power GNSS-Inertial Boards for High Precision and Control Applications
SUNNYVALE, Calif. - Trimble (NASDAQ: TRMB) introduced today a new...
YellowScan Unveils its Next Generation UAV-LiDAR Systems
YellowScan is committed to provide the most reliable integrated imaging systems...
senseFly to Launch Industry-Specific Solutions at INTERGEO 2017
Cheseaux-Lausanne, Switzerland– senseFly, the world’s leading producer of mapping...

April 21, 2011
Technology Will Drive Tomorrow’s Homeland Security

By André Doumitt, CEO of Geosemble Technologies (http://www.geosemble.com/), El Segundo, Calif.

The Department of Homeland Security's  all-encompassing mission is "to prevent and deter terrorist attacks,    protect against and respond to threats and hazards to the nation, and    secure our national borders".  Such scope makes it clear that technology  must play a central role in meeting that challenge. New sensing,    monitoring, data integration and display technologies are among the    primary drivers that will move homeland security forward. One benefit    will be technology's ability to free human resources from current    time-consuming, labor-intensive "discover and integrate" activities to  focus on the more critical "review and act" phases of threat   identification and emergency response.

Remote sensing technologies already contribute enhanced data to the  solution. Further, the maturation of today's satellite, aircraft and  land-based systems are providing sophisticated imagery on critical   infrastructure and fast-moving storms. Earth observation satellites with   thermal infrared (IR) sensors—combined with aircraft- and land-based   Doppler radar—track, analyze and predict hurricane behavior with amazing   accuracy.

For example, to understand the potential threats of a hurricane,   scientists developed the Sea, Lake and Overland Surges from Hurricanes   (SLOSH) model. The National Hurricane Center (NHC) uses this  computerized model to estimate anticipated storm surge heights and wind   velocities. Historical, hypothetical or predicted hurricanes are   modeled, taking into account an active hurricane's pressure, size,   forward speed, track and other factors. The system aggregates such data    to predict events such as the potential maximum storm surge flooding for  a given geographic area. SLOSH's predictive capabilities point toward  tomorrow's even more technologically sophisticated emergency response   solutions.

Overcoming Initial Challenges
One challenge has been the geographic data Tower of Babel in which  various state and local agencies hold their own data, with little   interagency access or interoperability.  Federal agencies have difficulty   coordinating within themselves and among each other. In a natural   disaster scenario, for example, it may be difficult to determine what   assets are at risk. The dynamic picture on the ground isn't always known   on a timely basis. The same holds true for a terrorist incident   management scenario in which critical utility, power, airport or   military facilities may be at risk. Fragmented data make it difficult to  assemble a clear picture of the asset distribution and make-up in a   given geographic site.

Remote sensing data contribute to a wide array of change-detection applications. Here two oblique aerial photographs show Galvestion, Texas, before (left) and after (right) Hurricane Ike made landfall. Yellow arrows mark features that appear in each image. Hurricane-induced waves and surge destroyed a pier and eroaded adjacent beaches.

 

For example, according to a Federal Emergency Management Agency (FEMA) report, when Hurricane Isabel   slammed into the eastern seaboard in 2003, the Delaware interagency   response team had little coordinated access to even commonly available   geographic information. Each office had to develop its own geographic   data and maps.  These duplicated efforts caused confusion and contributed   to an inefficient rescue effort.

Similarly, following the World Trade Center attacks, response teams  initially had to work with building-footprint data digitized from a   tourist map. It took several days to locate a floor plan. Eventually, three different floor-plan datasets emerged from three sources,  requiring more time to evaluate and settle on one.

Progress Will Trigger More Sophisticated Technology
Fortunately, there has been great progress in integrating data across   and within government agencies. The Homeland Security Infrastructure   Program is a leader with its network of some 17 federal agencies,
numerous state/local participants and several private companies.    Collectively pooling and integrating geographic data points is creating   a viable foundation-level national database.
Such massive databases will trigger the need for robust applications   that are smart enough to assimilate and analyze all that information.   Human analysis, already a daunting task as the volume of data expands,  will be replaced with "smart" computer applications that "think". The   future points to artificial intelligence (AI)—automated systems that   turn vast databases into actionable scenarios, such as telling emergency   response personnel what's where, what's relevant and what's new. Humans   will concentrate on the integrated picture an AI-based application   displays and be able to act on it in a timely manner.

The Future Is Just Around the Corner
As hurricanes threaten populated areas, a smart system will visualize on   maps and/or satellite images the threats to residents, structures,   protected wetlands, emergency routes, etc., in ways that accelerate   assessments and rapid-response decisions. AI already has gained   acceptance for its ability to perform complex analytics of   multidimensional data, especially in the finance industries. Algorithms,   the heart of AI systems, extract, analyze and integrate information that  would take days for an armada of humans to perform.

Already applied AI has proved its ability to ingest raw information from   diverse sources such as online RSS feeds and Web sites (even tweets),   online and offline databases, phone number lists, census data, personnel   rosters, commercial building occupants and more. In practice, emergency   response personnel tasked with protecting a critical structure could do   so by simply configuring an AI system as follows: "Monitor these 50 Web   sites where sensors or humans may post relevant reports, and display all   content, including police and other named investigative/law enforcement feeds, containing these nine keywords. Display any alerts issued about   activities within one mile of this structure, then send e-mail to my   Blackberry and  auto-summarize any content containing more than 50 words."  The number of   information sources, queries and responses can extend well beyond this   limited example. Thus, technology can create the conditions for   positively impacting the mitigation, preparedness, response and recovery   of emergency situations that emerge from threat events.

Tomorrow's Department of Homeland Security will be empowered by employing the technology that lets us know more sooner, which translates into more efficient predictions.  The day may come when taking preventative action in emergencies, whether in response to natural or man-made situations, becomes practical.  The path to such success includes many challenges, but intelligent data integration hold the real promise of delivering security against natural threats and hazards.

Comments are closed.