Accelerated Situational Awareness

KBR transformed the production of the U.S. National Land Cover Database (NLCD), a key data source used by scientists, resource managers and planners to inform critical policy and land management decisions across federal, state and private sectors. Historically, creating the NLCD data package required two to three years of intense manual processing, which greatly delayed the delivery of critical geospatial intelligence and impacted analyses and strategic policy decisions.

We reduced this production timeline to a few months and slashed processing costs by nearly 90% through introducing advanced AI/ML techniques such as automated feature extraction and deep learning-based classification. This dramatic improvement in efficiency, accuracy and scalability enabled USGS to deliver timely, high-quality land cover data and accelerate key decision timelines.

KBR applies advanced computer vision techniques to deblur images, particularly in the context of SAR and other complex imagery with native resolution that does not fully resolve features of interest. We accurately enhance image quality using deep learning architectures such as real-valued neural networks, complex-valued neural networks and custom implementations. Our AI-driven approaches significantly outperform traditional autofocus methods, resulting in higher fidelity imagery, improved feature extraction and more reliable downstream analyses and decision support.