Samantha T Arundel, PhD
Dr. Samantha T. Arundel is a research geographer in the Center of Excellence for Geospatial Information Science at the U.S. Geological Survey. Her research focuses on automating physical feature mapping and modeling using various techniques like traditional raster modeling, GEOBIA and machine learning.
Dr. Samantha T. Arundel (Sam) received her Ph.D. in geography from Arizona State University in 2000 and was an assistant and then associate professor at Northern Arizona University where her research focused on spatial modeling and automation of plant/climate relationships. In 2009, when she joined the USGS, she first served as raster specialist in the Ortho & Elevation section and as elevation and hydrography specialist for the Applied Research and Technology Branch. During this time, she led the contour generation development team in developing algorithms for automating contour production from 10-meter elevation data for the USTopo product; and served as the program manager for the automation of the National Elevation Dataset production, in its transition from Earth Resource Observation System (EROS) to the National Geospatial Technical Operations Center (NGTOC). In 2015, Sam moved to the Center of Excellence for Geospatial Information Science, the research section of the NGTOC, where she is a Research Geographer conducting research on automated terrain mapping and modeling using various techniques like traditional raster modeling, geographic object-based image analysis and machine learning.
Science and Products
GeoNatShapes: a natural feature reference dataset for mapping and AI training
Evaluation of classified ground points from National Agriculture Imagery program photogrammetrically derived point clouds
Assessing the utility of uncrewed aerial system photogrammetrically derived point clouds for land cover classification in the Alaska North Slope
GeoAI for spatial image processing
A guide to creating an effective big data management framework
Historical maps inform landform cognition in machine learning
Geomorphometric analysis of the Summit and Ridge classes of the Geographic Names Information System
GeoImageNet: A multi-source natural feature benchmark dataset for GeoAI and supervised machine learning
GeoAI and the future of spatial analytics
Deep learning detection and recognition of spot elevations on historic topographic maps
The evolution of geospatial reasoning, analytics, and modeling
GeoAI in the US Geological Survey for topographic mapping
Spatial data reduction through element -of-interest (EOI) extraction
Non-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Science and Products
GeoNatShapes: a natural feature reference dataset for mapping and AI training
Evaluation of classified ground points from National Agriculture Imagery program photogrammetrically derived point clouds
Assessing the utility of uncrewed aerial system photogrammetrically derived point clouds for land cover classification in the Alaska North Slope
GeoAI for spatial image processing
A guide to creating an effective big data management framework
Historical maps inform landform cognition in machine learning
Geomorphometric analysis of the Summit and Ridge classes of the Geographic Names Information System
GeoImageNet: A multi-source natural feature benchmark dataset for GeoAI and supervised machine learning
GeoAI and the future of spatial analytics
Deep learning detection and recognition of spot elevations on historic topographic maps
The evolution of geospatial reasoning, analytics, and modeling
GeoAI in the US Geological Survey for topographic mapping
Spatial data reduction through element -of-interest (EOI) extraction
Non-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.