Publications
Filter Total Items: 103
GeoImageNet: A multi-source natural feature benchmark dataset for GeoAI and supervised machine learning
The field of GeoAI or Geospatial Artificial Intelligence has undergone rapid development since 2017. It has been widely applied to address environmental and social science problems, from understanding climate change to tracking the spread of infectious disease. A foundational task in advancing GeoAI research is the creation of open, benchmark datasets to train and evaluate the performance of GeoAI
Authors
Wenwen Li, Sizhe Wang, Samantha Arundel, Chia-Yu Hsu
Open knowledge network roadmap: Powering the next data revolution
Open access to shared information is essential for the development and evolution of artificial intelligence (AI) and AI-powered solutions needed to address the complex challenges facing the nation and the world. The Open Knowledge Network (OKN), an interconnected network of knowledge graphs, would provide an essential public-data infrastructure for enabling an AI-driven future. It would facilitate
Authors
Chaitan Baru, Martin Halbert, Lara Campbell, Tess DeBlanc-Knowles, Jemin George, Wo Chang, Adam Pah, Douglas Maughan, Ilya Zaslavsky, Amanda Stathopoulos, Ellie Young, Kat Albrecht, Amit Sheth, Emanuel Sallinger, Katerine Osatuke, Angela Rizk-Jackson, Eric Jahn, Kenneth Berkowitz, Bandana Kar, Erica Smith, Krzystof Janowicz, Brian Handspicker, Esther Jackson, Lauren Sanders, Chengkai Li, Florence Hudson, Lilit Yeghiazarian, Cogan Shimizu, Glenn Ricart, Louiqa Raschid, Dalia E. Varanka, Greg Seaton, Luis Amaral, Oktie Hassanzadeh, Silviu Cucerzan, Matt Bishop, Ora Lassila, Sharat Israni, Matthew Lange, Pascal Hitzler, Ryan McGranaghan, Michael Cafarella, Paul Wormeli, Todd Bacastow, Sam Klein, Murat Omay, Sergio Baranzini, Ying Ding, Nariman Ammar
Scaling-up deep learning predictions of hydrography from IfSAR data in Alaska
The United States National Hydrography Dataset (NHD) is a database of vector features representing the surface water features for the country. The NHD was originally compiled from hydrographic content on U.S. Geological Survey topographic maps but is being updated with higher quality feature representations through flow-routing techniques that derive hydrography from high-resolution elevation data
Authors
Larry Stanislawski, Ethan J. Shavers, Alexander Duffy, Philip T. Thiem, Nattapon Jaroenchai, Shaowen Wang, Zhe Jiang, Barry J. Kronenfeld, Barbara P. Buttenfield
A geospatial knowledge graph prototype for national topographic mapping
Knowledge graphs are a form of database representation and handling that show the potential to better meet the challenges of data interoperability, semi-automated information reasoning, and information retrieval. Geospatial knowledge graphs (GKG) have at their core specialized forms of applied ontology that provide coherent spatial context to a domain of information including non-spatial attribute
Authors
Dalia E. Varanka
GeoAI and the future of spatial analytics
This chapter discusses the challenges of traditional spatial analytical methods in their limited capacity to handle big and messy data, as well as mining unknown or latent patterns. It then introduces a new form of spatial analytics—geospatial artificial intelligence (GeoAI)—and describes the advantages of this new strategy in big data analytics and data-driven discovery. Finally, a convergent spa
Authors
Wenwen Li, Samantha Arundel
Deep learning detection and recognition of spot elevations on historic topographic maps
Some information contained in historical topographic maps has yet to be captured digitally, which limits the ability to automatically query such data. For example, U.S. Geological Survey’s historical topographic map collection (HTMC) displays millions of spot elevations at locations that were carefully chosen to best represent the terrain at the time. Although research has attempted to reproduce t
Authors
Samantha Arundel, Trenton P. Morgan, Philip T. Thiem
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
The evolution of geospatial reasoning, analytics, and modeling
The field of geospatial analytics and modeling has a long history coinciding with the physical and cultural evolution of humans. This history is analyzed relative to the four scientific paradigms: (1) empirical analysis through description, (2) theoretical explorations using models and generalizations, (3) simulating complex phenomena and (4) data exploration. Correlations among developments in ge
Authors
Samantha Arundel, Wenwen Li
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 terrain featur
Authors
E. Lynn Usery, Samantha 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
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
Watersheds and drainage networks
This topic is an overview of basic concepts about how the distribution of water on the Earth, with specific regard to watersheds, stream and river networks, and waterbodies are represented by geographic data. The flowing and non-flowing bodies of water on the earth’s surface vary in extent largely due to seasonal and annual changes in climate and precipitation. Consequently, modeling the detailed
Authors
Larry Stanislawski, Ethan J. Shavers
The 4th paradigm in multiscale data representation: Modernizing the National Geospatial Data Infrastructure
The need of citizens in any nation to access geospatial data in readily usable form is critical to societal well-being, and in the United States (US), demands for information by scientists, students, professionals and citizens continue to grow. Areas such as public health, urbanization, resource management, economic development and environmental management require a variety of data collected from
Authors
Barbara P. Buttenfield, Larry Stanislawski, Barry J. Kronenfeld, Ethan J. Shavers