Transition of an area from the winter to the summer season, composed with Sentinel-2 images. Hints of the discriminative power of the snow cover can be noticed, as confirmed in the previous blog post.
Link to AWS S3 Bucket: http://eo-learn.sentinel-hub.com/
Dependency graph of the tasks in the workflow, provided by eo-learn.
Activation of the input (top) and the modulation (bottom) gate over the sequence of observations for a particular cell in the neural network. This cell has learned cloud masking and filtering, as input and modulation gates clearly show different activations on cloudy and non-cloudy observations. (Page 9 in https://www.researchgate.net/publication/322975904_Multi-Temporal_Land_Cover_Classification_with_Sequential_Recurrent_Encoders)
A visual representation of a temporal stack of Sentinel-2 images over a randomly selected area. The transparent pixels on the left imply missing data due to cloud coverage. The stack in the centre represents the pixel values after cloudy scene filtering and temporal interpolation with cloud masking (case A4), while the stack on the right shows the case without cloudy scene filtering and no cloud masking performed during interpolation (case A1).
This plot shows the number of EOPatches, which contain image data for each day of the year 2017 (blue). The overlaid lines (red) represent the optimal dates for the resampling choice, which were based on the Sentinel-2 acquisitions for the given AOI in 2017.
Comparison of different predictions of land cover classification. True colour image (top left), ground-truth land cover reference map (top right), prediction with the LightGBM model (bottom left), and prediction with the U-Net model (bottom right).


Sentinel Hub Blog

Stories from the next generation satellite imagery platform

Thanks to Devis Peressutti and Matej Aleksandrov

Matic Lubej

Written by

Data Scientist from Slovenia with a Background in Particle Physics.

Sentinel Hub Blog

Stories from the next generation satellite imagery platform

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