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Global Dynamic World Uncertainty layer #209
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Hi @Geethen |
Hi Sam, They all contain the same type of data/information just for different regions of the world. Ideally, This would be a single image collection. Specifically, each image contains the set length. i.e., a multiclass prediction based on the calibrated set-valued conformal predictor with alpha (tolerance level) = 0.1. Therefore, the predictions contain the actual class with a 90% probability. Here is a blog post (https://medium.com/@coachgeets/uncertainty-quantification-in-google-earth-engine-using-conformal-prediction-5e0dd3850564) describing conformal prediction for classification and a demo on how one can do this for any dynamic world scene and area. |
Contact Details
[email protected]
Dataset description
Using a modern uncertainty quantification method, conformal prediction, we have quantified the uncertainty for the first non-null dynamic world composite of 2020. Each pixel contains a value from 0 to 9 and represents the length of the prediction set (uncertainty) for a pixel. Each prediction set includes the correct landcover class with, in this case, a 90% probability. The more classes included in a set the greater the uncertainty. If the Dynamic World model was not confident for all 9 candidate classes, an empty set is returned (a value of 0). Therefore, a value of 0 corresponds to the highest uncertainty and a value of 1 corresponds to the lowest uncertainty. A set length value greater one is also associated with high uncertainty.
Research paper: https://arxiv.org/abs/2401.06421
The earth engine app can be used to derive uncertainty for any dynamic world scene
GEE app: https://ee-geethensingh.projects.earthengine.app/view/conformaluq
Earth Engine Snippet if dataset already in GEE
for example
Sample Code: Add a sample code maybe just adding your datasets in the code editor
Enter license information
CC-BY-4.0
Keywords
Uncertainty, Landcover, Dynamic World
Code of Conduct
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