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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. 

Over the past decade, colloidal quantum dot solar cells (CQD-SCs) have been developed rapidly, with their performances reaching over 16% power conversion efficiency. Accompanied by the development in materials engineering (CQD surface chemistry) and device physics (structures and defect engineering), CQD-SCs are moving towards commercialization. A broad overview of the requirements for commercialization is thus timely and imperative. Broad comprehension of structure engineering, upscaling techniques, stability and the manufacturing cost of CQD-SCs is necessary and should be established. In this review, the development of device structures is presented with their corresponding charge transfer mechanisms. Then, we overview the upscaling methods for the mass production of CQD-SCs. Comparisons between each of the upscaling techniques suggest the most advanced process close to industrialization. In addition, we have investigated the origin of the photovoltaic (PV) performance degradation. The possible degradation sources are categorized according to external environmental factors. Moreover, strategies for improving the stability of CQD-SCs are presented. In the conclusion, we have reviewed the cost-effectiveness of CQD-SCs in terms of the niche PV market. Step-wise manufacturing cost analysis for the commercial CQD-SCs is presented. In the conclusion, the future direction for environment-friendly CQD-SCs is discussed.