Achieving Efficient Storage and Communication via Collaboration
Earth observation (EO) constellations operated by organizations such as Planet, Google, and Amazon generate hundreds of terabytes of imagery every day. However, limited downlink bandwidth prevents immediate data transmission, forcing satellites to store large volumes of imagery onboard and often overwrite valuable data before it can be downlinked. Existing EO pipelines treat each capture independently and fail to exploit the substantial redundancy naturally present in EO constellations. In practice, two major redundancies dominate: (i) temporal stability, where consecutive images of the same area change minimally over time, and (ii) spatial overlap, where neighboring satellites capture largely identical ground regions. To address these inefficiencies, we present CoOrbit, a collaborative EO system that conserves onboard storage by retaining only changed and non-overlapping regions. CoOrbit combines lightweight onboard embedding differencing, TLE-based overlap inference, and adaptive reference embedding planning. We further extend CoOrbit with an application-driven design, allowing satellites to downlink only application-relevant tiles for even greater efficiency. Evaluations on satellite-grade GPUs and imagery demonstrate that CoOrbit achieves over 108.6× reduction in storage cost and 41.6× reduction in communication size compared to existing EO pipelines. Our evaluation also shows an additional order-of-magnitude reduction when the wildfire-driven design is applied.