no code implementations • 16 Jan 2024 • 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 Solis
To evaluate the performance of large AI vision models, especially Meta's Segment Anything Model (SAM), we implemented different instance segmentation pipelines that minimize the changes to SAM to leverage its power as a foundation model.
no code implementations • 25 Sep 2023 • Wenwen Li, Hyunho Lee, Sizhe Wang, Chia-Yu Hsu, Samantha T. Arundel
Vision foundation models are a new frontier in Geospatial Artificial Intelligence (GeoAI), an interdisciplinary research area that applies and extends AI for geospatial problem solving and geographic knowledge discovery, because of their potential to enable powerful image analysis by learning and extracting important image features from vast amounts of geospatial data.
no code implementations • 26 May 2018 • Xiran Zhou, Wenwen Li, Samantha T. Arundel, Jun Liu
To facilitate establishing an automatic approach for accessing the needed map, this paper reports our investigation into using deep learning techniques to recognize seven types of map, including topographic map, terrain map, physical map, urban scene map, the National Map, 3D map, nighttime map, orthophoto map, and land cover classification map.