Deep Neural Networks for applications in public safety

Deep Neural networks have revolutionized machine learning and in particular computer vision. The revolution was achieved by a combination of big data, graphical processing units and advances in numerical optimization. In this work we propose to extend and develop machine learning techniques, focusing on deep learning methods for public health and safety applications. We will use and extend deep learning methodology to deal with 3D seismic and electromagnetic data for signals that are emitted for public safety

Faculty Supervisor:

Eldad Haber

Student:

Jingrong Lin;Keegan Lensink;Tue Boesen

Partner:

Xtract AI

Discipline:

Geography / Geology / Earth science

Sector:

Professional, scientific and technical services

University:

University of British Columbia

Program:

Accelerate

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