Discover and read the best of Twitter Threads about #DynamicWorld
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With the release of #DynamicWorld and other #global maps from the last few years, I have some (rambling) thoughts on global products, the challenge of accuracy assessment, and why I think there is still a lot of room for high-quality local/regional predictive mapping (1)
First, these global products are super cool! I played around with Google's DynamicWorld 10 m land cover today and being able to see the probabilities for each class for each pixel is great and being able to do this for every #Sentinel image through time is really unique (2)
However, at #local scales, they can be hit-or-miss, and none are ideal for every need. Some fail to pass a visual inspection (the "eye test") and even the best products require (in my opinion) local quantitative accuracy assessment before use for decision making or in models. (3)
A thread about the newly launched #DynamicWorld landcover dataset by #Google. I had early access and explored this dataset in detail. You may be very excited about this dataset, but likely for the wrong reasons. Sharing some insights, potential use cases, and pitfalls. 1/n
First of all - what is it? It's a Landcover dataset based on Sentinel-2 data - but with a key difference. Rather than a static snapshot, it is a time series. *Every* Sentinel-2 scene is classified with class probabilities for 9 landcover classes. 2/n
It is an incredible technological feat. The dataset contains not just every Sentinel-2 scene from the archive, but every new scene is classified and made available in just a few minutes to all #EarthEngine users a through a dynamic collection 3/n developers.google.com/earth-engine/d…
