Mining High Resolution Earth Observation Data Cubes

Earth observation data is collected by ever-expanding fleets of satellites including Landsat1-8, Sentinel1 & Sentinel2, SPOT1-7 and WorldView1-3. These satellites generate at spatial resolutions (pixel size) from 30m to 31cm and provide revisit rates of as frequent as every 5 days. This allows us not only to look at high-resolution images of every corner of the Earth, but also to track events and observe change over time. During the past 5 years, medium spatial resolution satellite data (30 − 10m pixels) have developed very high temporal revisit frequencies of 5-16 days and spatial-temporal structures have been developed to manage these vast data sets. However, high resolution satellite images and rapidly increasing revisit rates create major data management and mining challenges. This work discusses six challenges of integrating observations at different times, from different sensors, at different spatial resolutions and different temporal frequencies into a unified Earth Observation Data Cube, that is, a tensor of location, time, and spectral bands. Challenges include creating a unified data cube from heterogeneous sensors, scaling geo-registration (mapping pixel between images), accounting for uncertainty across observations, imputing missing observations, broad area event detection, and ultimately, predicting the future state of our planet. With such a unified Earth Observation Data Cube in place, we describe potential application areas such as detecting anthropogenic land cover change, early warning of natural hazards, tracing movement of animals, finding missing airplanes, and rapid detection of forest fires.