Earth Observation Applications through Neural Embedding Compression from Foundation Models

Earth observation (EO) repositories comprise Petabytes of data. Due to their widespread use, these repositories experience extremely large volumes of data transfers. For example, users of the Sentinel Data Access System downloaded 78.6 PiB of data in 2022 alone. The transfer of such data volumes between data producers and consumers causes substantial latency and requires significant amounts of energy and vast storage capacities. This work introduces Neural Embedding Compression (NEC), a method that transmits compressed embeddings to users instead of raw data, greatly reducing transfer and storage costs. The approach uses general purpose embeddings from Foundation Models (FM), which can serve multiple downstream tasks and neural compression, which balances between compression rate and the utility of the embeddings. We implemented the method by updating a minor portion of the FM’s parameters (approximately 10%) for a short training period of about 1% of the original pre-training iterations. NEC’s effectiveness is assessed through two EO tasks: scene classification and semantic segmentation. When compared to traditional compression methods applied to raw data, NEC maintains similar accuracy levels while reducing data by 75% to 90%. Notably, even with a compression rate of 99.7%, there’s only a 5% decrease in accuracy for scene classification. In summary, NEC offers a resource-efficient yet effective solution for multi-task EO modeling with minimal transfer of data volumes.