Session
Session 11: Big Data From Small Satellites 2
Abstract
Earth Observation systems traditionally have followed the flagship mission approach. Surveillance missions carry large instruments, and focus on data quality and data volume to justify their investment. Earth science missions carry multiple instruments instead, and address a wide range of applications in order to justify their cost. Yet the “Big Data” value of these missions is often minimal, because the data sets cannot provide the temporal resolution *and* spatial resolution demanded by the majority of applications in trade, traffic, energy, disasters and even in providing useful information regarding slower phenomena like forestry and agriculture. Today it has become possible to do better than that, by utilising low cost small satellites in constellations with frequent global coverage. So far few small satellite Earth Observation constellations have been realised, but the parallel developments which have taken place in Cloud storage and Cloud computing have made it possible for EO data to be processed and distributed to worldwide users with diverse and different interests. When coupled with higher temporal resolution data from constellations this starts addressing specific opportunities. This paper focuses on the data richness that can be provided through small satellite constellations, and in particular focus on the OptiSAR and UrtheDaily constellations which aim to fuse optical, radar, video and global daily coverage of the Earth’s landmass. This, coupled with novel distribution and processing methods, will change the paradigm in how end users interact with Earth Observation data sets and the information that can be extracted automatically.
Presentation
Rich Data, Cheap Satellites
Earth Observation systems traditionally have followed the flagship mission approach. Surveillance missions carry large instruments, and focus on data quality and data volume to justify their investment. Earth science missions carry multiple instruments instead, and address a wide range of applications in order to justify their cost. Yet the “Big Data” value of these missions is often minimal, because the data sets cannot provide the temporal resolution *and* spatial resolution demanded by the majority of applications in trade, traffic, energy, disasters and even in providing useful information regarding slower phenomena like forestry and agriculture. Today it has become possible to do better than that, by utilising low cost small satellites in constellations with frequent global coverage. So far few small satellite Earth Observation constellations have been realised, but the parallel developments which have taken place in Cloud storage and Cloud computing have made it possible for EO data to be processed and distributed to worldwide users with diverse and different interests. When coupled with higher temporal resolution data from constellations this starts addressing specific opportunities. This paper focuses on the data richness that can be provided through small satellite constellations, and in particular focus on the OptiSAR and UrtheDaily constellations which aim to fuse optical, radar, video and global daily coverage of the Earth’s landmass. This, coupled with novel distribution and processing methods, will change the paradigm in how end users interact with Earth Observation data sets and the information that can be extracted automatically.
