## 3 Results
### Accuracy Assessment  
An accuracy assessment was conducted on the thirteen-class benthic habitat product using 2686 field data points that were excluded from the training of the classification algorithm (Figure 16). These points were collected in the same manner, at a variety of locations between 2010-2017 based on interpretation of GPS-referenced underwater video transects. Each of these points were cross-walked to the regional benthic habitat classification scheme. Once cross-walked, these points represented 8 of the 13 classes, as coral subclasses (e.g., reef fore, reef crest, reef back, as well as spur and groove) could not be distinguished from video footage and were collapsed into the coral/algae class. Results yielded an overall accuracy of 72% with a standard error of 1.3%, yielding a 3% confidence interval of 69-75%. This overall accuracy is calculated as the stratified (area-weighted) percentage of correctly classified sites in each sample drawn from the classified map [63]. It is an estimate of the percent of the total mapped area that classified/mapped correctly based on the comparison of the final map with the field gathered reference data. Table 9 shows the error matrix of the accuracy assessment. Producer's accuracy (errors of omission) and user's accuracy (errors of commission) are calculated and reported for each class. Producer's accuracy is a measure of how well real-world cover types can be classified. We calculated the area weighted proportion of correctly classified reference locations divided by the estimated proportion of area for the reference class (derived from the classification) and multiplied by 100 to express as a percent. User's accuracy reflects the reliability of the classification to the user and is the more relevant measure of the classification's actual utility in the field [64]. We calculated the area weighted proportion of correctly classified reference locations divided by the area weighted proportion of reference locations determined to be in each class, multiplied by 100. Figure 15: Final classification for 203,676 km\({}^{2}\) of shallow benthic habitat across the Insular Caribbean. These data are hosted at an online data portal found at CaribbeanMarineMaps.tnc.org (accessed on 19 October 2021), where data can be viewed and downloaded for advancing conservation actions throughout the Caribbean. Classes that exhibited the most confusion include sparse and dense seagrass as well as sparse and dense hardbottom algae. This confusion is not surprising as these classes can be very difficult to distinguish, particularly in deeper waters. Some classes, such as boulders and rocks, coral/algae, and muddy bottom, were accurately classified when considering a user's accuracy (i.e., objects were assigned to the correct class); however, these same classes reported a lower producer's accuracy (i.e., objects were left out of the class being evaluated). The large time range between when the field data was collected and changed in the benthic habitat composition could account for failure to observe and note differences in density between field data collection and satellite imaging. Another accuracy assessment was created after combining these pairs of classes, yielding six remaining classes. The estimate of the overall accuracy of this second assessment was 80% with a standard error of less than 1% yielding 2% confidence interval of 78-82%. The user's and producer's accuracy for this six-class accuracy assessment are reported in Table 10. [PERSON] and [PERSON] [65] provided several best practices for OBIA accuracy assessments, which they argue can be more complex than pixel-based accuracy assessment and provides more information, such as area-dependent classification accuracy or class-specific boundary errors. They recommend serious consideration into (i) the type of sampling unit (pixel or polygon), (ii) the types of accuracy indices (count-based or area-based), and (iii) the relevance of geometric quality assessment.