## 3 Results  
Accurate classification of remote sensing satellite images plays a crucial role in various applications, such as mapping, urban planning, and environmental monitoring. Key to the creation of robust and accurate image classification algorithms is the creation of large, geographically distributed labelled datasets that represent the complexity and diversity of the Earth's surface. The creation of such labelled datasets is time-consuming, resource intensive and uncertain, as it is difficult for remote sensing analysts to easily quantify and understand the complexity and diversity present within a geographically distributed unlabelled dataset. Labelling satellite images is necessary for both producing up-to-date maps and creating new labelled datasets for training new models. Finding selective images that contain desired features for a new training dataset can be a daunting task considering the volume of tiles over time and geographical location. Training models on satellite images incur a large cost in both training and finding suitable data to train on. The inputs to our labelling application are the satellite images that need to be given class labels, a trained model (e.g., the autoencoder we use for testing), and any prior labels (e.g., saved from a previous labelling exercise). The output will be class labels for the images. A key point is that the pre-trained model may provide good cluster coherency, but often there will be out-of-class samples that will negatively impact the labelling experience and hence the time to undertake labelling. Our interface helps this by allowing the user to explore the manifold by creating user directed two-dimensional embeddings from the high-dimensional embedding. The user can control the development of the embeddings, use the branch and merge feature (discussed later), switch between embedding types (UMAP or t-SNE) and extrapolate class labels forwards and backwards through embedding iterations. The case study is provided in cooperation with the UK Hydrographic Office. Our approach can be used with various applications in mind. For example, to quickly label similar samples we aim to bring large numbers of tiles with similar features together in the interface to apply a single class label in bulk. Another example would be to build a data set with a good distribution of rich and diverse features, where in that case we may focus on cluster boundaries to provide images with combinations of different features and differences from the cluster. **Manifold exploration**: We explain how the user interacts with a two dimensional embedding to gain an understanding of the higher-dimensional manifold and to label multiple images simultaneously with a class label. The user draws a selection box of a desired size over the plot (yellow box in Figure 4). Each point within the selection corresponds to one image, which is displayed in the image browser and located on the map. All images within a selection can be given the same class label. If any images are not part of the class, they can be individually reset or set to a different class using the image browser. The user can navigate the scatter plot by scaling and translating. The user drags the selection box so that points exit and neighbouring points enter the box. This simultaneously updates the image browser and map view, thus allowing the user to explore and become familiar with the structure of the manifold. Fine movement can help produce views where all images are consistent with one label, allowing a single label to be rapidly given to all points in the selection. In Figure 4, the user follows a path as indicated by the red arrows and yellow boxes, and sample images along the manifold from within the yellow boxes are presented in rows respective to the sampling region. These demonstrate how this model and two-dimensional embedding have placed images from the manifold.