## 3 Results
#### User Study  
In this section, we introduce a second application (Figure 11) to act as a comparison in a user study. We compare the efficiency of finding coastal patches utilising an image retrieval application as a benchmark, as our pipeline can be interpreted as a visual extension of image retrieval systems. The benchmark application produces \(K\) similar images based on a query image. The \(K\) nearest neighbours are determined by distance in the autoencoder latent space. The target query image is shown with the most similar \(K\) images. The user can label all images returned by the query or individually label each sample. Figure 10: **Left**, a branched embedding allows the user to quickly label sea images. **Right**, the labels (of both coastline and sea) are merged back into the original embedding. The newly labelled images from the branch are orange and grey within the original embedding. Guided by the labels, the user can make a large selection of sea images which can be labelled with one click. Figure 9: A cluster of high cloud, sea and coastline. To access the capability of each application we conducted a small user study with five participants. The user study consisted of each applicant labelling patches with any coastline utilising both applications. We presented the participants with forty initial \"seed\" images, as otherwise, the benchmark application required sorting through a list of 5963 patches to find an initial coastal patch to begin labelling from. The same \"seed\" patches are pre-labelled within our application as one class. The task was to label 160 patches with coastline using each application. We allowed participants to label more patches than required accounting for any scenarios where labels were assigned with the intention to refine after. The method to end the study was based on a small GUI element that let the researcher know that the labelling requirement had been met, allowing them to make an informed decision on when to let the participant stop labelling. We switched the order in which both applications were presented to each user in order to keep any familiarity gained with RS images consistent. As both t-SNE and Umap are stochastic algorithms we retained the initial randomisation seed between participants. In addition, we removed multi-threading and approximations utilised by the dimensionality reduction algorithms in order to keep consistency between participants. Times were recorded for each patch labelled. In order to produce the results, any patches labelled multiple times were removed with only the final label assignment considered. The results of the user study are shown in Figure 12. On average, the time taken to label using the benchmark application was 269 s compared to our application's 71 s, which is a factor of more than 3 times faster. Users utilising the benchmark application often found that only a small number of patches could be retrieved without the appearance of out-of-class images, which reduced the ability to label all samples quickly and efficiently therefore resulting in single sample labelling. In contrast, in our approach, the ability to optimise the box size and explore its surroundings produced similar images within the embedding, allowing the user to discern a suitable selection, which retained minimal out-of-sample patches and allowed the user to refine the selection with more confidence. The fastest user strategy recorded utilising our application searched the space before selecting clusters with more coherent samples and only a few outliers. In contrast, the other common labelling strategy used was panning a small selection area with continuous movement and refinement. The difference can be seen in Figure 12. The sharp increase in labels corresponds to labels applied to a larger selection area at once in contrast to the smoother gradient when labelling with a small selection strategy, respectively.