## 4 Discussion  
Designing a training and testing dataset for the development of computer vision algorithms for use in satellite imagery is time-consuming, resource-intensive and uncertain. It is difficult to be confident that a labelled dataset encompasses sufficient complexity and diversity to allow for the geographic generalisation of a trained algorithm to unseen areas. It is practically difficult to visualise large and geographically distributed datasets and \"debug\" the performance of a trained algorithm once its performance is assessed against a test dataset. Our tool offers new capabilities for us to reduce the time and resources required to label large datasets, visualise and understand the complexity and diversity of already labelled images, and assess model performance against clusters of similar image types, which might be geographically distributed over large and diverse environments. Overall, our tool will give us greater insight into the structure of unstructured satellite imagery data, supporting us to iterate model development with greater efficiency and confidence. With regard to processing and labelling datasets for future training of classification models, we have shown how structurally similar samples are clustered together and evolve along a manifold, allowing for the intuitive selection of samples from all geographical regions. Samples evolve as you move from a cluster, where samples with more unique features appear on the cluster boundaries. Users are able to select and label similar features and build up feature-rich data sets. Most works in remote sensing target specific regional locations to alleviate the vast complexity between tile locations and time. Utilising our tool, we show how analysis of multiple tile locations beforehand could contribute to providing balanced datasets for future classification tasks. User-led mass labelling could provide more contextual information based on the classification goal, e.g., lakes, farmland use, arboreal or coastline to name a few. Common image dataset tools in current works regard finding the top \(K\) images that correspond to a query set of images. Most, such as [PERSON] et al. [(40)], produce feature sets  
Figure 12: Line chart showing the difference in participants’ time taken to label coastline patches. The dashed lines represent times taken in the benchmark application. The solid lines represent our application. Each colour represents a different participant. using common image retrieval algorithms and fine-tuning with smaller patch sizes, but based on higher resolution labelled datasets. We have in comparison shown the feasibility of utilising manifold learning techniques to enable entire datasets and relationships between images to be displayed. This has been effective in mass labelling larger feature sets. When considering smaller local features, we utilise our branching methodology. Future work could also encode smaller patches with extra information regarding surrounding patches, increasing the focus on smaller features, but retaining a more global context. This of course would have to be balanced between computational efficiency for embeddings as larger latent spaces or sample rates increase the time complexity to calculate embeddings. In comparison, for better potential label separation, we could iteratively alter our higher dimensional space by pushing dissimilar images from an oracle or user's perspective away and pulling similar images closer. This process in machine learning is known as triplet loss [41]. Successfully applying triplet loss improves the downstream visualisation capabilities as manifolds separate clearly. However, this process would require retraining on the new high-dimensional embedding space, and constant oracle/human input where labels can heavily influence models' class separation. With such feature-rich patches, this could cause overlap between labelling categorisation, for example, where a particular patch includes multiple regions of interest, i.e., farmland and rivers.