## 3 Results
The block of images on the right shows three representative images from each of the indicated selection areas demonstrating the smooth evolution of the manifold from forest to urban land cover. Figure 5 shows 16 images in the browser window over the top of the scatterplot view. The images are from a small cluster on the left and show largely consistent clustering, but also demonstrate how the limits of a pre-trained model can affect labelling software. In this case, the reflectance and texture of cloud images have not been separated from the reflectance and texture of similar coastlines. The interface allows these labels to be quickly corrected individually. Users can label the data efficiently with a keyboard input as they explore the manifold. Figure 4: Example of images evolving from forest to dense urban whilst traversing through a manifold. Starting from the top of the two-dimensional embedding, subsequent samples were taken while moving the selection down. The respective contents of the samples are shown on the right. The accompanying video provides a better understanding of manifold evolution. Manifolds shown in the two-dimensional plot are formed by both the AE's expressiveness on the source images and the number of samples that the DR technique is provided with. When projecting all images from the SWED dataset, there is a clear separation between land and water clusters, but images containing both features (coastline) are on the edges of those clusters or form paths between the clusters. See the video in additional material for an example of coastline labelling. **User-directed reprojection of the embedding**: The two-dimensional scatter plot view may not be optimal due to two reasons. Firstly, the model's latent space may not be expressive enough. Secondly, which is the focus of this section, the dimension reduction techniques have insufficiently exploited the latent space to successfully create the two-dimensional embedding. We work under the assumption that the AI model is fixed due to the fact that training remote sensing models requires a tremendous amount of resources, and therefore we cannot alter the latent space interactively. Rather, we look to optimise the embedding instead. To find a better embedding we allow the user to direct re-embeddings by testing different parameters such as perplexity for t-SNE or \(N\) neighbours for UMAP. Increasing the value of both parameters allows for the DR algorithms to consider a larger neighbourhood, which provides more context to each embedded point. Conversely, if two manifolds exist in high dimensional space that overlaps frequently, a smaller neighbourhood of points would be preferable. Therefore, it is crucial that the user can interact with parameter selection (B in Figure 3). However, recalculation of the embeddings on the entire dataset requires a high computational cost and exploration cost as new embeddings require validation of information patterns by the user. Constant evaluation of newly projected manifolds can be mitigated with our tool because labels can be extrapolated when switching embeddings (between UMAP and t-SNE) and when altering any of the parameters. This consistency in class labels can provide the user with context, e.g., how previously neighbouring points may now cluster or spread across the updated embeddings. In Figure 6, t-SNE (top) has clustered mountainous images (with glacier), allowing the user to apply a label with a single key press. Below, Figure 6, UMAP (bottom) has not differentiated such images from other terrain. By labelling using t-SNE, and then changing embeddings, we can see how the labelled points redistribute and mix within other samples. It is not generally the case that t-SNE performs better, rather being able to switch embeddings or re-embed using new parameters allows the user to find appropriate clusters. Figure 5: A cluster of points are seen to be mostly coastline, but the model has placed two cloud images, which have a similar reflectance and texture, in the same cluster.