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Article analysis:

The article “SBSS: Stacking-Based Semantic Segmentation Framework for Very High-Resolution Remote Sensing Image” provides an overview of a new approach to semantic segmentation of VHR remote sensing images. The article presents the proposed SBSS framework as an effective and flexible solution to address the challenge posed by large variations in object scales in VHR images. The authors provide evidence from extensive experiments on four datasets to support their claims about the effectiveness and flexibility of SBSS.

The article appears to be reliable and trustworthy overall, as it provides detailed information about the proposed approach, its advantages over existing methods, and evidence from experiments to back up its claims. However, there are some potential biases that should be noted. For example, while the authors do mention some existing approaches such as MS test and SS test, they do not provide any comparison between these approaches and SBSS in terms of performance or other metrics. Additionally, while they do mention possible risks associated with using SBSS such as computational complexity control, they do not provide any details about how these risks can be mitigated or avoided. Furthermore, while they present evidence from experiments on four datasets to support their claims about SBSS’s effectiveness and flexibility, it would have been useful if they had provided more details about these experiments such as which metrics were used for evaluation or what parameters were used for training/testing etc., so that readers can better understand how exactly SBSS performs compared to existing methods.