## 4 Discussion
### Qualitative Assessment with Visual Inspection  
Commonly, a classification map is evaluated based on the accuracy reported [28]. In our study, we conducted a qualitative assessment by visually inspecting our cocoa map as a complement to the accuracy measures. The aim was to gain insight into our model's discriminating ability, which could not be revealed solely from the quantitative accuracy measures, although they were evaluated on an unseen and independent test dataset. The accuracy measures can only assess the degree of agreement with the reference data and not the actual correspondence to reality [28]. Therefore, we visually inspected our classification map overlaid on very high-spatial-resolution images to enable a finer interpretation. For feasibility reasons, we provided some illustrative cases of successful cocoa classification and misclassification. However, we need to bear in mind that such a qualitative approach cannot provide an exhaustive overview of the model's generalisation capacity. Instead, we could only point out some of the potentialities and weaknesses of the classified map, as derived from the individual example sites. Several land-use/cover studies have incorporated visual examples of their classification outcome compared with the satellite input image or images with very high-spatial-resolution [33; 126; 107; 113; 35; 106]. The authors of [113] performed an extensive visual inspection approach to communicate anomalous patterns and errors for their national-scale map, and the authors of [126] incorporated many visual examples to comment on the quality of their continental-scale map. The global study by [33] also provided site examples where their model succeeded or failed to detect oil palm plantations. Communicating the limitations of the classified outcome to the users with regard to the intended map purposes is of great importance [28]. However, we noticed that recently published cocoa studies provided only their final thematic map with no focus on reflecting on the quality of the classification outcome [45; 46; 47; 48; 67]. Only the authors of [34; 43] illustrated some classification examples but to a small extent. With a visual inspection, we showcased that our best-performing random forest was able to generalise well in some sites where full-sun cocoa trees were classified successfully and closed-canopy shade trees were delineated (Figure 4). In these examples, our random forest performed similarly to the CNN model developed by [PERSON] et al. [34]. Nonetheless, we detected a series of cocoa commission errors in our classified map (Figure 4(a)-d). Grassland, young oil palm trees and sparse vegetation constituted, among others, the main confusion with full-sun cocoa trees based on the illustrative examples. This is consistent with previous multi-class studies using optical data in Ghana, where cocoa was confused with oil palm and scrubland/grassland [43; 47]. A drawback of our binary classification was the lack of insight into inter-class confusion, whereas multi-class classification could provide a clearer understanding. Another type of commission error was observed across class borders and in the transitional zones between classes at some sites (Figure 11). These types of errors may correspond to mixed pixels. Other studies performed polygon erosion in their reference dataset to prevent errors caused by mixed pixels [102; 113; 117]. We did not apply this technique because of the limited area of our cocoa polygons. The visual inspection also enabled us to compare the discriminating capacity between a conventional machine learning classifier, i.e., random forest, and a deep learning model, i.e., CNN. In this way, we confirmed the presumed superiority of deep learning models that inherently take into account the context and spatial surroundings, as opposed to the traditional classifiers, which consider only the pixel-based information retrieved from handcrafted input data [127].