## 4 Discussion
Moreover, this kind of retrospective analysis provides valuable insights on the cost (e.g., time, knowledge, and labor) and benefits (classification accuracy) encoded in the workflow. We are no longer able to rely on low-level image attributes (e.g., color, texture, and shape) for describing LULC classes but require high-level semantic interpretations of image scenes. Image semantics are context sensitive, which means that semantics are not an intrinsic property captured during the image acquisition process but rather an emergent property of the interactions of human perception and the image content [77; 78; 79; 80]. GEOBIA's Achilles' heel--the semantic gap (lack of explicit link)--impedes repeatability, transferability, and interoperability of classification workflows. On the one hand, the rule-based classification reduces the semantic gap. On the other hand, it leads to plurality of solutions and hampers the transferability due to the need for significant operator involvement. For instance, in this study, another level of thematically-rich guano classification based on species (e.g., Adelie, gentoo, chinstrap, and possibly other seabirds) is critical for ecological model integration and will require deviations in the ruleset across image scenes or even possibly an array of new rules. From a conceptual perspective, we recognize GEOBIA's main challenge as a knowledge management problem and possibly mitigated with fuzzy ontologies [59; 74; 75; 81]. This serves as the key catalyst for our proposed conceptual sketch (Figure 2) for an \"image-to-assessment pipeline\" that leverages class explanation, remote sensing expert knowledge, domain ontology, multiscale image content modeling, and ecological model integration. We have proposed the concepts of target- and sensor-specific ruleset libraries and a repository of classified image objects as \"bag-of-image-objects\" (BIO) for refining and adapting classification workflows. Revisiting our findings, we should highlight that the deviations in the master ruleset across sites (also sensors) are not necessarily influenced by the variations in the class guano itself (Adelie _vs._ chinstrap penguins) but may be attributed to site-/region-specific characteristics such as existence of extensive vegetation. This precludes the use of a master ruleset that operates at the continent-scale but suggests instead an array of ruleset libraries representing regions, habitats, and possibly different sites within the continent. We emphasize the fact that a deep analysis on what factor(s) affected deviation(s) in a specific rule(s) seriously appeals for new components to the current experiment. Such analysis is beyond the scope of this study. Thus, here we provided plausible causes that hamper the transferability. We did not include gentoo penguins in our study even though their colonies also produce a guano stain visible in satellite imagery because their nesting densities are so highly variable that identification of gentoo colonies in imagery can be challenging even at well-mapped sites. For that reason, segmentation and classification of sites with gentoo penguins should be approached with particular caution until gentoo-specific methods can be developed. Future research will need to focus on a deeper understanding of the specific causes controlling the transferability, the engagement of machine learning algorithms, and a well-structured cognitive experiment to understand human visual reasoning in wildlife detection from remote sensing imagery.