Deep learning (DL)
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By Center of Excellence for Geospatial Information Science (CEGIS)
January 23, 2025
Deep learning (DL)
part of the Knowledge extraction theme from CEGIS
part of the Knowledge extraction theme from CEGIS
Deep learning (DL) is a subset of machine learning, with the difference that DL algorithms can automatically learn representations from data such as images, video, or text, without introducing human domain knowledge.
The word "deep" in deep learning represents the many layers of algorithms, or neural networks, that are used to recognize patterns in data.
Publications
Publications
You will find here a sampling of publications. More are available and are being published throughout the year.
Check back often or view our custom search for more!
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Automated deep learning-based point cloud classification on USGS 3DEP lidar data using transformer Automated deep learning-based point cloud classification on USGS 3DEP lidar data using transformer
The goal of the U.S. Geological Survey’s (USGS) 3D Elevation Program (3DEP) is to facilitate the acquisition of nationwide lidar data. Although data meet USGS lidar specifications, some point cloud tiles include noisy and incorrectly classified points. The enhanced accuracy of classified point clouds can improve support for many downstream applications such as hydrologic analysis, urban...Evaluation of classified ground points from National Agriculture Imagery program photogrammetrically derived point clouds Evaluation of classified ground points from National Agriculture Imagery program photogrammetrically derived point clouds
Studies have shown that digital surface models and point clouds generated by the United States Department of Agriculture’s National Agriculture Imagery Program (NAIP) can measure basic forest parameters such as canopy height. However, all measured forest parameters from these studies are evaluated using the differences between NAIP digital surface models (DSMs) and available lidar...Assessing the utility of uncrewed aerial system photogrammetrically derived point clouds for land cover classification in the Alaska North Slope Assessing the utility of uncrewed aerial system photogrammetrically derived point clouds for land cover classification in the Alaska North Slope
Uncrewed aerial systems (UASs) have been used to collect “pseudo field plot” data in the form of large-scale stereo imagery to supplement and bolster direct field observations to monitor areas in Alaska. These data supplement field data that is difficult to collect in such a vast landscape with a relatively short field season. Dense photogrammetrically derived point clouds are created...Remote sensing-based 3D assessment of landslides: A review of the data, methods, and applications Remote sensing-based 3D assessment of landslides: A review of the data, methods, and applications
Remote sensing (RS) techniques are essential for studying hazardous landslide events because they capture information and monitor sites at scale. They enable analyzing causes and impacts of ongoing events for disaster management. There has been a plethora of work in the literature mostly discussing (1) applications to detect, monitor, and predict landslides using various instruments and...Transferring deep learning models for hydrographic feature extraction from IfSAR data in Alaska Transferring deep learning models for hydrographic feature extraction from IfSAR data in Alaska
The National Hydrography Dataset (NHD) managed by the U.S. Geological Survey (USGS) is being updated with higher-quality feature representations through efforts that derive hydrography from 3DEP HR elevation datasets. Deriving hydrography from elevation through traditional flow routing and interactive methods is a complex, time-consuming process that must be tailored for different...
CEGIS - Denver, Colorado
CEGIS - Denver, Colorado
CEGIS - Rolla, Missouri
CEGIS - Rolla, Missouri
CEGIS - Denver, Colorado
CEGIS - Denver, Colorado
CEGIS - Rolla, Missouri
CEGIS - Rolla, Missouri