Publications
The CEGIS publications page is our one-stop collection of all publications from CEGIS authors, past and present.
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Pubs by theme
Publications are also grouped by subject theme and can be found via our Science pages.
Filter Total Items: 167
Assessment of a new GeoAI foundation model for floodinundation mapping Assessment of a new GeoAI foundation model for floodinundation mapping
Vision foundation models are a new frontier in GeoAI research because of their potential to enable powerful image analysis by analyzing and extracting important image features from vast amounts of geospatial data. This paper evaluates the performance of the first-of-its-kind geospatial foundation model, IBM-NASA’s Prithvi, to support a crucial geospatial analysis task: flood inundation...
Authors
Wenwen Li, Hyunho Lee, Sizhe Wang, Chia-Yu Hsu, Samantha Arundel
At what scales does a river meander? Scale-specific sinuosity (S3) metric for quantifying stream meander size distribution At what scales does a river meander? Scale-specific sinuosity (S3) metric for quantifying stream meander size distribution
Stream bend geometry is linked to terrain features, hydrologic and ecologic conditions, and anthropogenic forces. Knowledge of the distributions of geometric properties of streams advances understanding of changing landscape conditions and associated processes that operate over a range of spatial scales. Statistical decomposition of sinuosity in natural linear features has proven a...
Authors
Larry Stanislawski, Barry J. Kronenfeld, Barbara P. Buttenfield, Ethan J. Shavers
Reimagining standardization and geospatial interoperability in today’s GeoAI culture Reimagining standardization and geospatial interoperability in today’s GeoAI culture
Integrating Geospatial Artificial Intelligence (GeoAI) into our technological landscape has revolutionized our capacity to understand and engage with the world. However, the burgeoning adoption of GeoAI applications has underscored the imperative of data, format, and conveyance standardization and enhancing geospatial interoperability. This vision paper delves into the intricacies of the...
Authors
Samantha Arundel, Wenwen Li, Bryan B Campbell
A guide to creating an effective big data management framework A guide to creating an effective big data management framework
Many agencies and organizations, such as the U.S. Geological Survey, handle massive geospatial datasets and their auxiliary data and are thus faced with challenges in storing data and ingesting it, transferring it between internal programs, and egressing it to external entities. As a result, these agencies and organizations may inadvertently devote unnecessary time and money to convey...
Authors
Samantha Arundel, Kevin G McKeehan, Bryan B Campbell, Andrew N. Bulen, Philip T. Thiem
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...
Authors
Larry V. Stanislawski, Nattapon Jaroenchai, Shaowen Wang, Ethan J. Shavers, Alexander Duffy, Philip T. Thiem, Zhe Jiang, Adam Camerer
Generalization quality metrics to support multiscale mapping: Hausdorff and average distance between polylines Generalization quality metrics to support multiscale mapping: Hausdorff and average distance between polylines
Large geospatial datasets must often be generalized for analysis and display at reduced scales. Automated methods including artificial intelligence and deep learning are being applied to this problem, but the results are often analyzed on the basis of limited and subjective measures. To better support automation, a project is underway to develop a robust Python toolkit for computing...
Authors
Barry J. Kronenfeld, Larry Stanislawski, Barbara P. Buttenfield, Ethan J. Shavers
Historical maps inform landform cognition in machine learning Historical maps inform landform cognition in machine learning
No abstract available.
Authors
Samantha Arundel, Sinha Gaurav, Wenwen Li, David P. Martin, Kevin G McKeehan, Philip T. Thiem
Automated mapping of culverts, bridges, and dams Automated mapping of culverts, bridges, and dams
Accurate maps of built structures around stream channels, such as dams, culverts, and bridges, are vital in monitoring infrastructure, risk management, and hydrologic modeling. Hydrologic modeling is essential for research and decisionmaking related to infrastructure and development planning, emergency management, ecology, and developing hydrographic data. Technological advances in...
Authors
Ethan J. Shavers, Larry Stanislawski, Joel Schott, Zachary Brosseau
Geomorphometric analysis of the Summit and Ridge classes of the Geographic Names Information System Geomorphometric analysis of the Summit and Ridge classes of the Geographic Names Information System
This research aims to conduct a geosemantic comparison of landforms classified in the Summit and Ridge feature classes in the Geographic Names Information System (GNIS). The comparison is based on a 2D shape analysis of manually delineated polygons produced by USGS staff to correspond to 33,304 Summit and 8,006 Ridge features. Five shape measures were chosen for this specific...
Authors
Sinha Gaurav, Samantha Arundel, Romim Somadder, David P. Martin, Kevin G McKeehan
Comparing line feature morphology with scale specific sinuosity distributions: A modified earth mover’s distance Comparing line feature morphology with scale specific sinuosity distributions: A modified earth mover’s distance
No abstract available.
Authors
Barry J. Kronenfeld, Barbara Buttenfield, Ethan J. Shavers, Larry Stanislawski
GeoImageNet: A multi-source natural feature benchmark dataset for GeoAI and supervised machine learning 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...
Authors
Wenwen Li, Sizhe Wang, Samantha Arundel, Chia-Yu Hsu
Open knowledge network roadmap: Powering the next data revolution 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...
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