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Samantha T. Arundel
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2020 – today
- 2024
- [j8]Wenwen Li
, Chia-Yu Hsu
Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model's Generalizability in Permafrost Mapping. Remote. Sens. 16(5): 797 (2024) - [e1]Song Gao, Gengchen Mai, Shawn D. Newsam, Lexie Yang, Dalton D. Lunga, Di Zhu, Bruno Martins, Samantha T. Arundel:
Proceedings of the 7th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2024, Atlanta, GA, USA, 29 October 2024 - 1 November 2024. ACM 2024, ISBN 979-8-4007-1176-3 [contents] - [i4]Wenwen Li, Chia-Yu Hsu, Sizhe Wang, Yezhou Yang, Hyunho Lee, Anna Liljedahl, Chandi Witharana, Yili Yang, Brendan M. Rogers, Samantha T. Arundel, Matthew B. Jones, Kenton McHenry, Patricia Solís:
Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model's Generalizability in Permafrost Mapping. CoRR abs/2401.08787 (2024) - 2023
- [j7]Wenwen Li, Sizhe Wang, Samantha T. Arundel, Chia-Yu Hsu:
GeoImageNet: a multi-source natural feature benchmark dataset for GeoAI and supervised machine learning. GeoInformatica 27(3): 619-640 (2023) - [j6]Wenwen Li, Sizhe Wang, Samantha T. Arundel, Chia-Yu Hsu:
Correction to: GeoImageNet: a multi-source natural feature benchmark dataset for GeoAI and supervised machine learning. GeoInformatica 27(3): 643 (2023) - [j5]Samantha T. Arundel
A guide to creating an effective big data management framework. J. Big Data 10(1): 146 (2023) - [c5]Samantha T. Arundel
Reimagining standardization and geospatial interoperability in today's GeoAI culture. GeoAI@SIGSPATIAL 2023: 83-84 - [c4]Wenwen Li
Assessment of a new GeoAI foundation model for flood inundation mapping. GeoAI@SIGSPATIAL 2023: 102-109 - [i3]Wenwen Li, Hyunho Lee, Sizhe Wang, Chia-Yu Hsu, Samantha T. Arundel:
Assessment of IBM and NASA's geospatial foundation model in flood inundation mapping. CoRR abs/2309.14500 (2023) - 2022
- [j4]E. Lynn Usery
GeoAI in the US Geological Survey for topographic mapping. Trans. GIS 26(1): 25-40 (2022) - 2020
- [j3]Samantha T. Arundel
Automated location correction and spot height generation for named summits in the coterminous United States. Int. J. Digit. Earth 13(12): 1570-1584 (2020) - [j2]Samantha T. Arundel
GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning. Trans. GIS 24(3): 556-572 (2020)
2010 – 2019
- 2019
- [j1]Xiran Zhou
A spatio-contextual probabilistic model for extracting linear features in hilly terrains from high-resolution DEM data. Int. J. Geogr. Inf. Sci. 33(4): 666-686 (2019) - 2018
- [c3]Gaurav Sinha, Samantha T. Arundel, Torsten Hahmann
The Landform Reference Ontology (LFRO): A Foundation for Exploring Linguistic and Geospatial Conceptualization of Landforms (Short Paper). GIScience 2018: 59:1-59:7 - [i2]Xiran Zhou, Wenwen Li, Samantha T. Arundel, Jun Liu:
Deep Convolutional Neural Networks for Map-Type Classification. CoRR abs/1805.10402 (2018) - [i1]Samantha T. Arundel, Wenwen Li, Xiran Zhou:
The effect of resolution on terrain feature extraction. PeerJ Prepr. 6: e27072 (2018) - 2017
- [c2]Samantha T. Arundel, Gaurav Sinha:
Validating GEOBIA Based Terrain Segmentation and Classification for Automated Delineation of Cognitively Salient Landforms. COSIT (Workshops/Posters) 2017: 9-14 - [c1]Gaurav Sinha, Samantha T. Arundel, Kathleen Stewart, David M. Mark, Torsten Hahmann
A Reference Landform Ontology for Automated Delineation of Depression Landforms from DEMs. COSIT (Workshops/Posters) 2017: 111-116
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