Validating GEOBIA Based Terrain Segmentation and Classification for Automated Delineation of Cognitively Salient Landforms. - Samantha T. Arundel; Gaurav Sinha (COSIT (Workshops/Posters), 2017)

A Reference Landform Ontology for Automated Delineation of Depression Landforms from DEMs. - Gaurav Sinha; Samantha T. Arundel; Kathleen Stewart; David M. Mark; Torsten Hahmann; Boleslo E. Romero; Alexandre Sorokine; E. Lynn Usery; Grant McKenzie (COSIT (Workshops/Posters), 2017)

The Landform Reference Ontology (LFRO): A Foundation for Exploring Linguistic and Geospatial Conceptualization of Landforms (Short Paper). - Gaurav Sinha; Samantha T. Arundel; Torsten Hahmann; E. Lynn Usery; Kathleen Stewart; David M. Mark (GIScience, 2018)

Deep Convolutional Neural Networks for Map-Type Classification. - Xiran Zhou; Wenwen Li 002; Samantha T. Arundel; Jun Liu 018 (CoRR, 2018)

The effect of resolution on terrain feature extraction. - Samantha T. Arundel; Wenwen Li 002; Xiran Zhou (PeerJ Prepr., 2018)

A spatio-contextual probabilistic model for extracting linear features in hilly terrains from high-resolution DEM data. - Xiran Zhou; Wenwen Li 002; Samantha T. Arundel (Int. J. Geogr. Inf. Sci., 2019)

Automated location correction and spot height generation for named summits in the coterminous United States. - Samantha T. Arundel; Gaurav Sinha (Int. J. Digit. Earth, 2020)

GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning. - Samantha T. Arundel; Wenwen Li 002; Sizhe Wang (Trans. GIS, 2020)

GeoAI in the US Geological Survey for topographic mapping. - E. Lynn Usery; Samantha T. Arundel; Ethan Shavers; Lawrence V. Stanislawski; Philip Thiem; Dalia Varanka (Trans. GIS, 2022)

Assessment of a new GeoAI foundation model for flood inundation mapping. - Wenwen Li 002; Hyunho Lee; Sizhe Wang; Chia-Yu Hsu 001; Samantha T. Arundel (GeoAI@SIGSPATIAL, 2023)

Reimagining standardization and geospatial interoperability in today's GeoAI culture. - Samantha T. Arundel; Wenwen Li 002; Bryan B. Campbell (GeoAI@SIGSPATIAL, 2023)

Assessment of IBM and NASA's geospatial foundation model in flood inundation mapping. - Wenwen Li 002; Hyunho Lee; Sizhe Wang; Chia-Yu Hsu 001; Samantha T. Arundel (CoRR, 2023)

GeoImageNet: a multi-source natural feature benchmark dataset for GeoAI and supervised machine learning. - Wenwen Li 002; Sizhe Wang; Samantha T. Arundel; Chia-Yu Hsu 001 (GeoInformatica, 2023)

Correction to: GeoImageNet: a multi-source natural feature benchmark dataset for GeoAI and supervised machine learning. - Wenwen Li 002; Sizhe Wang; Samantha T. Arundel; Chia-Yu Hsu 001 (GeoInformatica, 2023)

A guide to creating an effective big data management framework. - Samantha T. Arundel; Kevin G. McKeehan; Bryan B. Campbell; Andrew N. Bulen; Philip Thiem (J. Big Data, 2023)

Report of the 7th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI 2024). - Song Gao 001; Gengchen Mai; Shawn D. Newsam; Lexie Yang; Dalton D. Lunga; Di Zhu 004; Bruno Martins 001; Samantha T. Arundel (ACM SIGSPATIAL Special, 2024)

Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model's Generalizability in Permafrost Mapping. - Wenwen Li 002; Chia-Yu Hsu 001; Sizhe Wang; Yezhou Yang; Hyunho Lee; Anna Liljedahl; Chandi Witharana; Yili Yang; Brendan M. Rogers; Samantha T. Arundel; Matthew B. Jones; Kenton McHenry; et al. (Remote. Sens., 2024)

GIScience in the Era of Artificial Intelligence: A Research Agenda Towards Autonomous GIS. - Zhenlong Li; Huan Ning; Song Gao 001; Krzysztof Janowicz; Wenwen Li 002; Samantha T. Arundel; Chaowei Yang; Budhendra Bhaduri; Shaowen Wang 001; A-Xing Zhu; Mark Gahegan; Shashi Shekhar 001; et al. (CoRR, 2025)