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Article analysis:

This article is a comprehensive review of supervised object-based land-cover image classification, providing an extensive meta-analysis of research results from 254 experimental cases described in 173 scientific papers. The authors provide useful data on supervised object-based image classification, including general characteristics of the studies (e.g., geographic range of relevant institutes, preferred journals) and the relationships between factors of interest (e.g., spatial resolution and study area or optimal segmentation scale, accuracy and number of targeted classes). Furthermore, they identify methods that may advance supervised object-based image classification (e.g., deep learning and type-2 fuzzy techniques).

The article is well written and provides a thorough overview of the topic at hand. It is based on reliable sources (173 scientific papers) which are referenced throughout the text to support its claims. The authors have also provided useful data on supervised object-based image classification which can be used to further explore the effects of varied factors on this type of classification.

The only potential bias in this article is that it does not present both sides equally; instead it focuses solely on the benefits and advantages associated with supervised object-based land cover image classification without exploring any potential risks or drawbacks associated with this method. However, given that this is a review paper rather than an argumentative one, this bias can be overlooked as it does not detract from the overall quality or reliability of the article itself.