Johns Hopkins APL Modernization Measures Focus on Real-Time Disaster Response

Johns Hopkins Applied Physics Laboratory is working on ways to boost its capabilities when it comes to disaster response.

Jeffrey Freeman, APL research scientist, said in a statement Tuesday they are looking into creating an intelligent and autonomous system that allows context-dependent situational awareness for disaster and emergency response and recovery.

Among APL’s goals are full automation of data collection and fast analysis of heterogeneous data, which include video, audio, global positioning system, free text, structured text, images, radar and other data sources as defined by U.S. government agencies and nonprofit organizations that are involved in disaster response.

According to Freeman, sponsors can benefit from advanced machine learning methods such as interoperable and autonomous data systems, and deep learning and nontraditional data sources such as drone and LIDAR sensing.

Freeman said APL also developed SOCRATES — an artificial intelligence suite of over 200 machine learning algorithms for processing link interference, behavior analysis, probabilistic models, correlation analysis, community finding and centrality analysis — to manage main data functions.

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