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The National Science Foundation’s Arctic Data Center is the primary data repository for NSF-funded research conducted in the Arctic. There are major challenges in discovering and interpreting resources in a repository containing data as heterogeneous and interdisciplinary as those in the Arctic Data Center. This paper reports on advances in cyberinfrastructure at the Arctic Data Center that help address these issues by leveraging semantic technologies that enhance the repository’s adherence to the FAIR data principles and improve the Findability, Accessibility, Interoperability, and Reusability of digital resources in the repository. We describe the Arctic Data Center’s improvements. We use semantic annotation to bind metadata about Arctic data sets with concepts in web-accessible ontologies. The Arctic Data Center’s implementation of a semantic annotation mechanism is accompanied by the development of an extended search interface that increases the findability of data by allowing users to search for specific, broader, and narrower meanings of measurement descriptions, as well as through their potential synonyms. Based on research carried out by the DataONE project, we evaluated the potential impact of this approach, regarding the accessibility, interoperability, and reusability of measurement data. Arctic research often benefits from having additional data, typically from multiple, heterogeneous sources, that complement and extend the bases – spatially, temporally, or thematically – for understanding Arctic phenomena. These relevant data resources must be 'found', and 'harmonized' prior to integration and analysis. The findings of a case study indicated that the semantic annotation of measurement data enhances the capabilities of researchers to accomplish these tasks. 

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 tremendous success achieved by Large Language Models (LLMs) as the foundation models for language tasks, this paper discusses the challenges of building foundation models for geospatial artificial intelligence (GeoAI) vision tasks. 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. A series of prompt strategies were developed to test SAM’s performance regarding its theoretical upper bound of predictive accuracy, zero-shot performance, and domain adaptability through fine-tuning. The analysis used two permafrost feature datasets, ice-wedge polygons and retrogressive thaw slumps because (1) these landform features are more challenging to segment than man-made features due to their complicated formation mechanisms, diverse forms, and vague boundaries; (2) their presence and changes are important indicators for Arctic warming and climate change. The results show that although promising, SAM still has room for improvement to support AI-augmented terrain mapping. The spatial and domain generalizability of this finding is further validated using a more general dataset EuroCrops for agricultural field mapping. Finally, we discuss future research directions that strengthen SAM’s applicability in challenging geospatial domains. 

Data are available for download at http://arcticdata.io/data/10.18739/A2KW57K57 Permafrost can be indirectly detected via remote sensing techniques through the presence of ice-wedge polygons, which are a ubiquitous ground surface feature in tundra regions. Ice-wedge polygons form through repeated annual cracking of the ground during cold winter days. In spring, the cracks fill in with snowmelt water, creating ice wedges, which are connected across the landscape in an underground network and that can grow to several meters depth and width. The growing ice wedges push the soil upwards, forming ridges that bound low-centered ice-wedge polygons. If the top of the ice wedge melts, the ground subsides and the ridges become troughs and the ice-wedge polygons become high-centered. Here, a Convolutional Neural Network is used to map the boundaries of individual ice-wedge polygons based on high-resolution commercial satellite imagery obtained from the Polar Geospatial Center. This satellite imagery used for the detection of ice-wedge polygons represent years between 2001 and 2021, so this dataset represents ice-wedge polygons mapped from different years. This dataset does not include a time series (i.e. same area mapped more than once). The shapefiles are masked, reprojected, and processed into GeoPackages with calculated attributes for each ice-wedge polygon such as circumference and width. The GeoPackages are then rasterized with new calculated attributes for ice-wedge polygon coverage such a coverage density. This release represents the region classified as “high ice” by Brown et al. 1997. The dataset is available to explore on the Permafrost Discovery Gateway (PDG), an online platform that aims to make big geospatial permafrost data accessible to enable knowledge-generation by researchers and the public. The PDG project creates various pan-Arctic data products down to the sub-meter and monthly resolution. Access the PDG Imagery Viewer here: https://arcticdata.io/catalog/portals/permafrost Data limitations in use: This data is part of an initial release of the pan-Arctic data product for ice-wedge polygons, and it is expected that there are constraints on its accuracy and completeness. Users are encouraged to provide feedback regarding how they use this data and issues they encounter during post-processing. Please reach out to the dataset contact or a member of the PDG team via support@arcticdata.io.