Radiant Earth, whose CEO Anne Hale Miglarese I was lucky enough to see speak at the RSPSoc conference last year, partnered with Amazon in order to provide more ‘geodiverse’ training data for machine learning models. I think this is timely, as the AI4EO paradigm sets in. The availability of Sentinel 2 Analysis Ready Data from s3, as well as the ability for partial reads of this data using gdal, is the preferred option vs. Google Earth Engine for me for geodevelopment, so I’m delighted on these continuing data releases. I’ve been reading about rastervision, and look forward to sinking my teeth into this data with that as a supporting tool to see what kind of learning can be done!
Geodiversity is required for reliable modelling (source)
Past Sentinel 2 data, there’s so much opportunity to shift thinking on how to develop AI4EO models, extending to other metrics such as air quality (for instance from Sentinel3 SLSTR).
In the air quality world, we would do well to better value data gathered and research done in “data-gap places.” Otherwise, we are at high risk of convincing ourselves that a parochial view projected onto the globe represents true scientific understanding. https://t.co/IBq62L20Js
— Chris Hasenkopf🐰 (@sciencerely) April 23, 2019
Keep an eye on this space – I’ll do an jupyter notebook or similar exploring the data once I get the chance!
