U.S. Army Corps of Engineers announces finalization of nationwide permits
Jan. 07, 2026 | 
News Release
The U.S. Army Corps of Engineers announced today that it will reissue 56 existing nationwide permits and issue one new permit for work in wetlands and...
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A Soldier and three other civilian men document events in an airfield tower.
USACE Black Start Exercise Brings Light to Readiness
Nov. 20, 2025 | 
News
Increased installation readiness is the goal of the Black Start Exercise Program, a joint U.S. Army Corps of Engineers-led initiative, to test and...
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Army Executes POTUS Directive on Ambler Road Project
Oct. 23, 2025 | 
News Release
President Donald J. Trump has approved the appeal of the Alaska Industrial Development and Export Authority (AIDEA), directing the U.S. Army Corps of...
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USACE introduces new Regulatory Request System module
Sep. 22, 2025 | 
News Release
The U.S. Army Corps of Engineers announced today the launch of a new “No Permit Required” module on its Regulatory Request System (RRS), an innovative...
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Army Corps of Engineers begins implementing policy to increase America’s energy generation efficiency
Sep. 22, 2025 | 
News Release
Assistant Secretary of the Army for Civil Works Adam Telle today directed the U.S. Army Corps of Engineers to weigh whether energy projects that might...
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park ranger in foreground looks out toward blue lake from the shore.
Army Corps of Engineers waives fees and invites volunteers to participate in National Public Lands Day, Sept. 27
Sep. 15, 2025 | 
News Release
The U.S. Army Corps of Engineers announced today that it will waive day use fees normally charged at boat launch ramps and swimming beaches at its...
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  • PUBLICATION NOTICE: Understanding State-of-the-Art Material Classification through Deep Visualization

    Abstract: Neural networks (NNs) excel at solving several complex, non-linear problems in the area of supervised learning. A prominent application of these networks is image classification. Numerous improvements over the last few decades have improved the capability of these image classifiers. However, neural networks are still a black-box for solving image classification and other sophisticated tasks. A number of experiments conducted look into exactly how neural networks solve these complex problems. This paper dismantles the neural network solution, incorporating convolution layers, of a specific material classifier. Several techniques are utilized to investigate the solution to this problem. These techniques look at specifically which pixels contribute to the decision made by the NN as well as a look at each neuron’s contribution to the decision. The purpose of this investigation is to understand the decision-making process of the NN and to use this knowledge to suggest improvements to the material classification algorithm.
  • PUBLICATION NOTICE: Understanding State-of-the-Art Material Classification through Deep Visualization

    Abstract: Neural networks (NNs) excel at solving several complex, non-linear problems in the area of supervised learning. A prominent application of these networks is image classification. Numerous improvements over the last few decades have improved the capability of these image classifiers. However, neural networks are still a black-box for solving image classification and other sophisticated tasks. A number of experiments conducted look into exactly how neural networks solve these complex problems. This paper dismantles the neural network solution, incorporating convolution layers, of a specific material classifier. Several techniques are utilized to investigate the solution to this problem. These techniques look at specifically which pixels contribute to the decision made by the NN as well as a look at each neuron’s contribution to the decision. The purpose of this investigation is to understand the decision-making process of the NN and to use this knowledge to suggest improvements to the material classification algorithm.

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