E. Lynn Usery (Former Employee)
Science and Products
Filter Total Items: 60
Transfer learning with convolutional neural networks for hydrological streamline delineation Transfer learning with convolutional neural networks for hydrological streamline delineation
Hydrological streamline delineation is critical for effective environmental management, influencing agriculture sustainability, river dynamics, watershed planning, and more. This study develops a novel approach to combining transfer learning with convolutional neural networks that capitalize on image-based pre-trained models to improve the accuracy and transferability of streamline...
Authors
Nattapon Jaroenchai, Shaowen Wang, Larry Stanislawski, Ethan J. Shavers, Zhe Jiang, Vasit Sagan, E. Lynn Usery
Weakly supervised spatial deep learning for Earth image segmentation based on imperfect polyline labels Weakly supervised spatial deep learning for Earth image segmentation based on imperfect polyline labels
In recent years, deep learning has achieved tremendous success in image segmentation for computer vision applications. The performance of these models heavily relies on the availability of large-scale high-quality training labels (e.g., PASCAL VOC 2012). Unfortunately, such large-scale high-quality training data are often unavailable in many real-world spatial or spatiotemporal problems...
Authors
Zhe Jiang, Wenchong He, M. S. Kirby, Arpan Man Sainju, Shaowen Wang, Larry Stanislawski, Ethan J. Shavers, E. Lynn Usery
GeoAI in the US Geological Survey for topographic mapping GeoAI in the US Geological Survey for topographic mapping
Geospatial artificial intelligence (GeoAI) can be defined broadly as the application of artificial intelligence methods and techniques to geospatial data, processes, models, and applications. The application of these methods to topographic data and phenomena is a focus of research in the US Geological Survey (USGS). Specifically, the USGS has researched and developed applications in...
Authors
E. Lynn Usery, Samantha T. Arundel, Ethan J. Shavers, Larry Stanislawski, Philip T. Thiem, Dalia E. Varanka
Extensibility of U-net neural network model for hydrographic feature extraction and implications for hydrologic modeling Extensibility of U-net neural network model for hydrographic feature extraction and implications for hydrologic modeling
Accurate maps of regional surface water features are integral for advancing ecologic, atmospheric and land development studies. The only comprehensive surface water feature map of Alaska is the National Hydrography Dataset (NHD). NHD features are often digitized representations of historic topographic map blue lines and may be outdated. Here we test deep learning methods to automatically...
Authors
Larry V. Stanislawski, Ethan J. Shavers, Shaowen Wang, Zhe Jiang, E. Lynn Usery, Evan Moak, Alexander Duffy, Joel Schott
Spatial data reduction through element -of-interest (EOI) extraction Spatial data reduction through element -of-interest (EOI) extraction
Any large, multifaceted data collection that is challenging to handle with traditional management practices can be branded ‘Big Data.’ Any big data containing geo-referenced attributes can be considered big geospatial data. The increased proliferation of big geospatial data is currently reforming the geospatial industry into a data-driven enterprise. Challenges in the big spatial data...
Authors
Samantha T. Arundel, E. Lynn Usery
An attention U-Net model for detection of fine-scale hydrologic streamlines An attention U-Net model for detection of fine-scale hydrologic streamlines
Surface water is an irreplaceable resource for human survival and environmental sustainability. Accurate, finely detailed cartographic representations of hydrologic streamlines are critically important in various scientific domains, such as assessing the quantity and quality of present and future water resources, modeling climate changes, evaluating agricultural suitability, mapping...
Authors
Zewei Xu, Shaowen Wang, Larry Stanislawski, Zhe Jiang, Nattapon Jaroenchai, Arpan Man Sainju, Ethan J. Shavers, E. Lynn Usery, Li Chen, Zhiyu Li, Bin Su
Science and Products
Filter Total Items: 60
Transfer learning with convolutional neural networks for hydrological streamline delineation Transfer learning with convolutional neural networks for hydrological streamline delineation
Hydrological streamline delineation is critical for effective environmental management, influencing agriculture sustainability, river dynamics, watershed planning, and more. This study develops a novel approach to combining transfer learning with convolutional neural networks that capitalize on image-based pre-trained models to improve the accuracy and transferability of streamline...
Authors
Nattapon Jaroenchai, Shaowen Wang, Larry Stanislawski, Ethan J. Shavers, Zhe Jiang, Vasit Sagan, E. Lynn Usery
Weakly supervised spatial deep learning for Earth image segmentation based on imperfect polyline labels Weakly supervised spatial deep learning for Earth image segmentation based on imperfect polyline labels
In recent years, deep learning has achieved tremendous success in image segmentation for computer vision applications. The performance of these models heavily relies on the availability of large-scale high-quality training labels (e.g., PASCAL VOC 2012). Unfortunately, such large-scale high-quality training data are often unavailable in many real-world spatial or spatiotemporal problems...
Authors
Zhe Jiang, Wenchong He, M. S. Kirby, Arpan Man Sainju, Shaowen Wang, Larry Stanislawski, Ethan J. Shavers, E. Lynn Usery
GeoAI in the US Geological Survey for topographic mapping GeoAI in the US Geological Survey for topographic mapping
Geospatial artificial intelligence (GeoAI) can be defined broadly as the application of artificial intelligence methods and techniques to geospatial data, processes, models, and applications. The application of these methods to topographic data and phenomena is a focus of research in the US Geological Survey (USGS). Specifically, the USGS has researched and developed applications in...
Authors
E. Lynn Usery, Samantha T. Arundel, Ethan J. Shavers, Larry Stanislawski, Philip T. Thiem, Dalia E. Varanka
Extensibility of U-net neural network model for hydrographic feature extraction and implications for hydrologic modeling Extensibility of U-net neural network model for hydrographic feature extraction and implications for hydrologic modeling
Accurate maps of regional surface water features are integral for advancing ecologic, atmospheric and land development studies. The only comprehensive surface water feature map of Alaska is the National Hydrography Dataset (NHD). NHD features are often digitized representations of historic topographic map blue lines and may be outdated. Here we test deep learning methods to automatically...
Authors
Larry V. Stanislawski, Ethan J. Shavers, Shaowen Wang, Zhe Jiang, E. Lynn Usery, Evan Moak, Alexander Duffy, Joel Schott
Spatial data reduction through element -of-interest (EOI) extraction Spatial data reduction through element -of-interest (EOI) extraction
Any large, multifaceted data collection that is challenging to handle with traditional management practices can be branded ‘Big Data.’ Any big data containing geo-referenced attributes can be considered big geospatial data. The increased proliferation of big geospatial data is currently reforming the geospatial industry into a data-driven enterprise. Challenges in the big spatial data...
Authors
Samantha T. Arundel, E. Lynn Usery
An attention U-Net model for detection of fine-scale hydrologic streamlines An attention U-Net model for detection of fine-scale hydrologic streamlines
Surface water is an irreplaceable resource for human survival and environmental sustainability. Accurate, finely detailed cartographic representations of hydrologic streamlines are critically important in various scientific domains, such as assessing the quantity and quality of present and future water resources, modeling climate changes, evaluating agricultural suitability, mapping...
Authors
Zewei Xu, Shaowen Wang, Larry Stanislawski, Zhe Jiang, Nattapon Jaroenchai, Arpan Man Sainju, Ethan J. Shavers, E. Lynn Usery, Li Chen, Zhiyu Li, Bin Su