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: 142
Segment anything model can not segment anything: Assessing AI foundation model's generalizability in permafrost mapping
This paper assesses trending AI foundation models, especially emerging computer vision foundation models and their performance in natural landscape feature segmentation. While the term foundation model has quickly garnered interest from the geospatial domain, its definition remains vague. Hence, this paper will first introduce AI foundation models and their defining characteristics. Built upon the
Authors
Wenwen Li, Chia-Yu Hsu, Sizhe Wang, Yezhou Yang, Hyunho Lee, Anna Liljedahl, Chandi Witharana, Yili Yang, Brendan M. Rogers, Samantha Arundel, Matthew B. Jones, Kenton McHenry, Patricia Solis
GeoAI for science and the science of GeoAI
This paper reviews trends in GeoAI research and discusses cutting-edge ad- vances in GeoAI and its roles in accelerating environmental and social sciences. It ad- dresses ongoing attempts to improve the predictability of GeoAI models and recent re- search aimed at increasing model explainability and reproducibility to ensure trustworthy geospatial findings. The paper also provides reflections on t
Authors
Wenwen Li, Samantha Arundel, Song Gao, Michael F. Goodchild, Yingjie Hu, Shaowen Wang, Alexander Zipf
Grammar To Graph, an approach for semantic transformation of annotations to triples
Linguistic representation of geographic knowledge is semantically complex and particularly challenging when employing geographic information technology to automate interpreted analysis dealing with unstructured knowledge. This study describes an approach called GrammarToGraph (G2G) that applies dependency grammar rules through natural language processing to transform annotation data into structure
Authors
Dalia E. Varanka, Emily Abbott
Automated deep learning-based point cloud classification on USGS 3DEP lidar data using transformer
The goal of the U.S. Geological Survey’s (USGS) 3D Elevation Program (3DEP) is to facilitate the acquisition of nationwide lidar data. Although data meet USGS lidar specifications, some point cloud tiles include noisy and incorrectly classified points. The enhanced accuracy of classified point clouds can improve support for many downstream applications such as hydrologic analysis, urban planning,
Authors
Jung-Kuan (Ernie) Liu, Rongjun Qin, Shuang Song
Evaluation of classified ground points from National Agriculture Imagery program photogrammetrically derived point clouds
Studies have shown that digital surface models and point clouds generated by the United States Department of Agriculture’s National Agriculture Imagery Program (NAIP) can measure basic forest parameters such as canopy height. However, all measured forest parameters from these studies are evaluated using the differences between NAIP digital surface models (DSMs) and available lidar digital terrain
Authors
Jung-Kuan (Ernie) Liu, Samantha Arundel, Ethan J. Shavers
Assessing the utility of uncrewed aerial system photogrammetrically derived point clouds for land cover classification in the Alaska North Slope
Uncrewed aerial systems (UASs) have been used to collect “pseudo field plot” data in the form of large-scale stereo imagery to supplement and bolster direct field observations to monitor areas in Alaska. These data supplement field data that is difficult to collect in such a vast landscape with a relatively short field season. Dense photogrammetrically derived point clouds are created and are faci
Authors
Jung-Kuan (Ernie) Liu, Rongjun Qin, Samantha Arundel
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
Authors
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
Authors
Samantha Arundel, Kevin G McKeehan, Wenwen Li, Zhining Gu
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 mapping. Th
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
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
Authors
Larry Stanislawski, Barry J. Kronenfeld, Barbara P. Buttenfield, Ethan J. Shavers
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 evolving
Authors
Samantha Arundel, Wenwen Li, Bryan B Campbell
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
Authors
Samantha Arundel, Kevin G McKeehan, Bryan B Campbell, Andrew N. Bulen, Philip T. Thiem