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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 image anal
Hessah Albanwan, Rongjun Qin, Jung-Kuan (Ernie) Liu

GeoAI for spatial image processing

The development of digital image processing, as a subset of digital signal processing, depended upon the maturity of photography and image science, introduction of computers, discovery and advancement of digital recording devices, and the capture of digital images. In addition, government and industry applications in the Earth and medical sciences were paramount to the growth of the technology. Fr
Samantha Arundel, Kevin G McKeehan, Wenwen Li, Zhining Gu

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 longstanding ch
Larry Stanislawski, Barry J. Kronenfeld, Barbara P. Buttenfield, Ethan J. Shavers

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 data without
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

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 hydrogeomorphic
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

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 objective metr
Barry J. Kronenfeld, Larry Stanislawski, Barbara P. Buttenfield, Ethan J. Shavers

Historical maps inform landform cognition in machine learning

No abstract available.
Samantha Arundel, Sinha Gaurav, Wenwen Li, David P. Martin, Kevin G McKeehan, Philip T. Thiem

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 geomorphometry-based
Sinha Gaurav, Samantha Arundel, Romim Somadder, David P. Martin, Kevin G McKeehan

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
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
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
Larry Stanislawski, Ethan J. Shavers, Alexander Duffy, Philip T. Thiem, Nattapon Jaroenchai, Shaowen Wang, Zhe Jiang, Barry J. Kronenfeld, Barbara P. Buttenfield