E. Lynn Usery (Former Employee)
Science and Products
Filter Total Items: 58
Know, Know Where, KnowWhereGraph: A densely connected, cross-domain knowledge graph and geo-enrichment service stack for applications in environmental intelligence
Knowledge graphs are a novel paradigm for the representation, retrieval, and integration of data from highly heterogeneous sources. Within just a few years, knowledge graphs andKnowledge graphs (KGs) are a novel paradigm for the representation, retrieval, and integration of data from highly heterogeneous sources. Within just a few years, KGs and their supporting technologies have become a core com
Authors
Krzysztof Janowicz, Pascal Hitzler, Wenwen Li, Dean Rehberger, Mark P. Schildhauer, Rui Zhu, Cogan Shimizu, Colby K Fisher, Ling Cai, Gengchen Mai, Joseph Zalewski, Lu Zhou, Shirly Stephen, Seila Gonzalez, Anna Lopez-Carr, Andrew Schroeder, Dave Smith, E. Lynn Usery, Dalia E. Varanka, Dawn Wright, Sizhe Wang, Yuanyuan Tian, Zilong Liu, Meilin Shi, Anthony D'Onofrio, Zhining Gu, Kitty Currier
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 in earth s
Authors
Zhe Jiang, Wenchong He, M. S. Kirby, Arpan Man Sainju, Shaowen Wang, Larry Stanislawski, Ethan J. Shavers, E. Lynn Usery
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 NHD features are often digitized representations of historic topographic map blue lines and may be outdated.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 extract s
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
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 domain can b
Authors
Samantha Arundel, E. Lynn Usery
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 flood inundati
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
Semantically enabling map projections knowledge
Map projections are an area of cartography with a firm mathematical foundation for their creation and display providing a basis for a knowledge representation. Using only variations on a single equation set, an infinite number of projections can be created, but less than 100 are in active use. Because each projection preserves specific characteristics, such as area, angles, global look, or a compr
Authors
E. Lynn Usery
Improving geospatial query performance of an interoperable geographic situation-awareness system (IGSAS) for disaster response
Disaster response operations require fast and coordinated actions based on the real-time disaster situation information. Although Volunteered Geographic Information (VGI) or crowdsourced geospatial data applications have demonstrated to be valuable tools for gathering real-time disaster situation information, they only provide limited utility for disaster response coordination because of the lack
Authors
Chuanrong Zhang, Tian Zhao, E. Lynn Usery, Dalia E. Varanka, Weidong Li
A system design for implementing advanced feature descriptions for a map knowledge base
A prototype system to explore Linked Data that semantically integrates geospatial data in various formats from different publication sources with data from The National Map of the U.S. Geological Survey is presented. The focus is on accessing advanced feature descriptions for data from The National Map with data coreferenced from other sources. The prototype uses Geoserver to access The National M
Authors
Matthew Wagner, Dalia E. Varanka, E. Lynn Usery
U.S. Geological Survey accomplishments in cartography 2015-2019
The U.S. Geological Survey (USGS), the United States' official national topographic mapping organization, is building and maintaining geographic databases for fundamental base geographic layers of land cover, structures, boundaries, hydrography, geographic names, transportation, elevation, and orthoimagery as The National Map. Data from the 3D Elevation Program, the National Hydrography Dataset an
Authors
E. Lynn Usery
Problems of Large Spatial Databases
Large spatial databases often labeled as geospatial big data exceed the capacity of commonly used computing systems as a result of data volume, variety, velocity, and veracity. Additional problems also labeled with V’s are cited, but the four primary ones are the most problematic and focus of this chapter (Li et al., 2016, Panimalar et al., 2017). Sources include satellites, aircraft and drone pl
Authors
E. Lynn Usery
Topographic mapping evolution: From field and photogrammetric data collection to GIS production and Linked Open Data
Whither the topographic map? Topographic mapping historically has been approached as a map factory operation through the period 1879-1990. During this time, data were field and photogrammetrically collected; cartographically verified and annotated creating a compilation manuscript; further edited, generalized, symbolized, and produced as a graphic output product using lithography, or more recently
Authors
E. Lynn Usery, Dalia E. Varanka, Larry Davis
GNIS-LD: Serving and visualizing the Geographic Names Information System Gazetteer as linked data
In this dataset description paper we introduce the GNIS-LD, an authoritative and public domain Linked Dataset derived from the Geographic Names Information System (GNIS) which was developed by the U.S. Geological Survey (USGS) and the U.S. Board on Geographic Names. GNIS provides data about current, as well as historical, physical, and cultural geographic features in the United States. We describe
Authors
Blake Regalia, Krzysztof Janowicz, Gengchen Mai, Dalia E. Varanka, E. Lynn Usery
Science and Products
- Publications
Filter Total Items: 58
Know, Know Where, KnowWhereGraph: A densely connected, cross-domain knowledge graph and geo-enrichment service stack for applications in environmental intelligence
Knowledge graphs are a novel paradigm for the representation, retrieval, and integration of data from highly heterogeneous sources. Within just a few years, knowledge graphs andKnowledge graphs (KGs) are a novel paradigm for the representation, retrieval, and integration of data from highly heterogeneous sources. Within just a few years, KGs and their supporting technologies have become a core comAuthorsKrzysztof Janowicz, Pascal Hitzler, Wenwen Li, Dean Rehberger, Mark P. Schildhauer, Rui Zhu, Cogan Shimizu, Colby K Fisher, Ling Cai, Gengchen Mai, Joseph Zalewski, Lu Zhou, Shirly Stephen, Seila Gonzalez, Anna Lopez-Carr, Andrew Schroeder, Dave Smith, E. Lynn Usery, Dalia E. Varanka, Dawn Wright, Sizhe Wang, Yuanyuan Tian, Zilong Liu, Meilin Shi, Anthony D'Onofrio, Zhining Gu, Kitty CurrierWeakly 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 in earth sAuthorsZhe Jiang, Wenchong He, M. S. Kirby, Arpan Man Sainju, Shaowen Wang, Larry Stanislawski, Ethan J. Shavers, E. Lynn UseryExtensibility 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 extract sAuthorsLarry V. Stanislawski, Ethan J. Shavers, Shaowen Wang, Zhe Jiang, E. Lynn Usery, Evan Moak, Alexander Duffy, Joel SchottSpatial 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 domain can bAuthorsSamantha Arundel, E. Lynn UseryAn 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 flood inundatiAuthorsZewei Xu, Shaowen Wang, Larry Stanislawski, Zhe Jiang, Nattapon Jaroenchai, Arpan Man Sainju, Ethan J. Shavers, E. Lynn Usery, Li Chen, Zhiyu Li, Bin SuSemantically enabling map projections knowledge
Map projections are an area of cartography with a firm mathematical foundation for their creation and display providing a basis for a knowledge representation. Using only variations on a single equation set, an infinite number of projections can be created, but less than 100 are in active use. Because each projection preserves specific characteristics, such as area, angles, global look, or a comprAuthorsE. Lynn UseryImproving geospatial query performance of an interoperable geographic situation-awareness system (IGSAS) for disaster response
Disaster response operations require fast and coordinated actions based on the real-time disaster situation information. Although Volunteered Geographic Information (VGI) or crowdsourced geospatial data applications have demonstrated to be valuable tools for gathering real-time disaster situation information, they only provide limited utility for disaster response coordination because of the lackAuthorsChuanrong Zhang, Tian Zhao, E. Lynn Usery, Dalia E. Varanka, Weidong LiA system design for implementing advanced feature descriptions for a map knowledge base
A prototype system to explore Linked Data that semantically integrates geospatial data in various formats from different publication sources with data from The National Map of the U.S. Geological Survey is presented. The focus is on accessing advanced feature descriptions for data from The National Map with data coreferenced from other sources. The prototype uses Geoserver to access The National MAuthorsMatthew Wagner, Dalia E. Varanka, E. Lynn UseryU.S. Geological Survey accomplishments in cartography 2015-2019
The U.S. Geological Survey (USGS), the United States' official national topographic mapping organization, is building and maintaining geographic databases for fundamental base geographic layers of land cover, structures, boundaries, hydrography, geographic names, transportation, elevation, and orthoimagery as The National Map. Data from the 3D Elevation Program, the National Hydrography Dataset anAuthorsE. Lynn UseryProblems of Large Spatial Databases
Large spatial databases often labeled as geospatial big data exceed the capacity of commonly used computing systems as a result of data volume, variety, velocity, and veracity. Additional problems also labeled with V’s are cited, but the four primary ones are the most problematic and focus of this chapter (Li et al., 2016, Panimalar et al., 2017). Sources include satellites, aircraft and drone plAuthorsE. Lynn UseryTopographic mapping evolution: From field and photogrammetric data collection to GIS production and Linked Open Data
Whither the topographic map? Topographic mapping historically has been approached as a map factory operation through the period 1879-1990. During this time, data were field and photogrammetrically collected; cartographically verified and annotated creating a compilation manuscript; further edited, generalized, symbolized, and produced as a graphic output product using lithography, or more recentlyAuthorsE. Lynn Usery, Dalia E. Varanka, Larry DavisGNIS-LD: Serving and visualizing the Geographic Names Information System Gazetteer as linked data
In this dataset description paper we introduce the GNIS-LD, an authoritative and public domain Linked Dataset derived from the Geographic Names Information System (GNIS) which was developed by the U.S. Geological Survey (USGS) and the U.S. Board on Geographic Names. GNIS provides data about current, as well as historical, physical, and cultural geographic features in the United States. We describeAuthorsBlake Regalia, Krzysztof Janowicz, Gengchen Mai, Dalia E. Varanka, E. Lynn Usery