## 1 Introduction
IR algorithms apply a form of top \(K\) retrieval, where \(K\) denotes the number of samples to be retrieved. Solutions for encoding functions within IR also utilise AE architectures for unsupervised feature extraction [18]. However, this approach limits the user to a variable number of images returned and requires an initial query image. In order to consider all images within a dataset there are further dimensionality reduction techniques that find manifolds of similar images. This approach has been considered with regard to time-series data [19; 20] and time-series clustering [21]. Manifold projection involves mapping high-dimensional data into lower dimensions and can be formulated as attempting to keep pairwise distances as defined in the higher dimensional space as similar as possible to pairwise distances within the lower dimensional space, such as classical multi-dimensional scaling (MDS) [22]. Dimensions in this context refer to the number of variables representing the information. Different approaches to reduction may look to find low-dimensional representations of nonlinear manifolds or patterns in high-dimensional space [23]. For singular manifold representation, common algorithms include MDS, isometric mapping (ISOMAP), and locally linear neighbourhood (LLE) [257 ]. When considering multiple manifolds in high dimensional space, efficiency dictates comparing pairwise points in local space rather than the global pairwise distance between all points. A similarity matrix can be created that focuses on connecting similar neighbours and an assumption that many short paths (small geodesic distances) between points indicates these points are likely part of the same underlying structure in the data. Existing algorithms that do so for multiple manifolds include variations of ISOMAP, LLE, Hessian LLE or Laplacian Eigenmaps, each with varying suitability to certain tasks and limitations [26]. Stochastic Neighbourhood Embedding (SNE) computes the locality between points in a neighbourhood converting samples into probabilistic samples based on the Euclidean distance [27]. The low-dimensional embedding is created by enforcing the low-dimensional probabilities to be similar to the higher-dimensional embeddings. SNE proved capable of preserving local neighbourhoods in lower dimensional space; however, it suffered from many points overlapping in the projection, referred to as the crowding problem [28]. t-SNE was introduced to allow for effective visualization of multiple, related, high-dimensional manifolds by revealing structures at many different scales [29]. To overcome the crowding problem t-SNE was introduced where a long tail distribution was introduced to the lower dimensions, in contrast, the use of a heavy tail distribution has been shown to tighten clusters [29; 30]. UMAP [31] is similar to t-SNE, in that both use gradient descent to optimize the embedding space. UMAP construction is based on a fuzzy graph approach of the high dimensional embedding in an attempt to accurately represent the topology. The low-dimensional embedding is then optimised to conform to the high-dimensional graphs and weights. UMAP aims to provide a more global context to the low-dimensional embedding comparatively to t-SNE, which can be attributed to the initialization of the low dimensional embedding space created by a fuzzy graph [32]. Many recent papers debate the similarities and differences between these two algorithms. In summary, both t-SNE and UMAP produce similar results when given similar initial conditions and both have been shown to be sensitive to starting parameters and low embedding [33]. Our approach presents the user with a mass labelling interface enabling them to explore a dataset of satellite images. Class labels can be applied to multiple images at a time during user selection of those images in the interface. The classes are determined by the user and correspond to the visual features they want to extract, separate or label.