Critically important phenomena in Earth’s magnetosphere often occur briefly, or in small spatial regions. These processes are sampled with orbiting spacecraft or by fixed ground observatories and so rarely appear in data. Identifying such intervals can be an incredibly time consuming task. We apply a novel, powerful method by which two dimensional data can be automatically processed and embeddings created that contain key features of the data. The distance between embedding vectors serves as a measure of similarity. We apply the state-of-the-art method to two example datasets: MMS electron velocity distributions and auroral all sky images. We show that the technique creates embeddings that group together visually similar observations. When provided with novel example images the method correctly identifies similar intervals: when provided with an electron distribution sampled during an encounter with an electron diffusion region the method recovers similar distributions obtained during two other known diffusion region encounters. Similarly, when provided with an interesting auroral structure the method highlights the same structure observed from an adjacent location and at other close time intervals. The method promises to be a useful tool to expand interesting case studies to multiple events, without requiring manual data labeling. Further, the models could be fine-tuned with relatively small set of labeled example data to perform tasks such as classification. The embeddings can also be used as input to deep learning models, providing a key intermediary step—capturing the key features within the data.