## 3 Results
**Branch and merge**: As an alternative to global embeddings, we introduced the ability to branch the dataset. Branching focuses the dimensionality reduction on the currently selected data. The two-dimensional embedding is optimised with respect to those samples as only those samples are considered. The computational cost is less due to the reduced number of samples when searching for optimal embedding parameters. The data for a particular branch is determined by the user indicating which class label or classes are to be used for that branch. All images labelled as that class form the branch. The user may select data directly on the two-dimensional plot using the selection box, apply a class label, and send that set of points to a branch. To self-contain branches, all embeddings utilise the UMAP or t-SNE algorithms for optimisation in their own sand-boxed environment without affecting any other dataset or branch. Working with this reduced set of samples has two benefits. First, embeddings are faster to compute when varying parameters during exploration. Secondly, as the samples will have been determined by the user to have similarities, re-embedding them will effectively amplify the remaining feature differences yielding new clusters based on the alternate features. The new embedded clusters may separate better than the original given the same pre-trained model output. Figure 6: In this particular case, t-SNE (**top**) effectively clusters mountainous regions compared to UMAP (**bottom**) using the same embedding. Figure 7 shows a branch of data from Figure 5, which largely represents images with only sea and varying coverage of coastline. In the global embedding, they clump together in a smaller cluster in which it would be difficult to make selections that separate these classes for labelling. Branching out these points allows a re-embedded of the points, resulting in the embedding seen in Figure 7. In this case, the points scattered along the top of the new plot are predominantly the images that contain just the sea, allowing the user to make large selections of tens to hundreds of images and directly apply one class label. Moving along the top of the plot and to the right (Figure 8) introduces a new wind farm feature where we can see a regular grid of white turbines against the dark blue sea suggesting the user would be able to quickly label sea infrastructure as a separate class if desired. Further around the plot (Figure 9) there is a mix of coast, high cloud and sea that has not been effectively separated and therefore would be time-consuming to label. This can also be branched out into its own where the sea is more effectively separated in this new embedding (Figure 10 (left)). Labels are then merged back into the main embedding (Figure 10 (right)) showing the separation (or lack of) between these classes. This allows fast labelling of the data, which also allows the user to visualise the class boundary, allowing  
Figure 8: To the right of the plot from Figure 7 there are some images of wind farms. Figure 7: Left points in the left of Figure 5 are branched out into their own embeddings. an assessment of model performance by suggesting where the model may be failing to provide good class separation. We also find that branching is effective in another way. If a user wants to label patches with multiple features, for example, coastal areas with farmland present, they can branch and embed all coastal patches together with a selection of patches that contain only farmland. The embedding will create unique clusters for each feature; however, samples that have both features will lie on the edge of the cluster closest to the cluster that contains the other feature. In this case, samples with both coastline and farmland will be on the edge of the coastline cluster closest to the cluster containing purely farmland. In this way, branching can act as a way to query image features in clusters by leveraging the model's general ability to distinguish between them.