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Resampling large zarr datasets to lower resolution and creating pyramids in which each level has increasing resolution.

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willem0boone/Edito_resampling_datasets

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Edito Resampling datasets

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This project is part of EDITO-INFRA (Grant agreement ID: 101101473):

  • T7.3: End-to-end demonstrator for aquaculture and maritime industry.

Author: Willem Boone | contact: [email protected]

Goal

Summary of Demonstrator

The demonstrator use case (DUC) consists of a smartviewer that hosts a model to predict habitat suitability based on environmental living conditions. The smartviewer is maintained in a separate repository GitHub - Edito_model_viewer and is based on Carbonplan its Mapping seaweed farming potential / GitHub - seaweed-farming-web.

In this demonstrator, habitat suitability is calculated using a deterministic model that uses minimum and maximum thresholds on the environmental variables. The environmental parameters that are used are:

  • Sea surface temperatur
  • Sea surface salinity
  • Bathymetry

The thresholds for all variables can be adopted using slider widgets. On any changing parameter, the suitability map is updated and rendered in the viewer. Using a time slider, environmental parameters for several future climate scenarios can be accessed and converted in suitability maps.

Data formatting

The environmental variable dataset used by the smartviewer, need to be provided in a specific format. To create this dataset, different sources and storage from Edito data lake are used. Two pipelines were required:

  • Downscaling large .zarr datasets to lower resolution. E.g. the bathymetry
  • dataset is around 20GB, which is to large for the demonstrator purpose.
  • Creating pyramids in which each level has increasing resolution (for optimal zooming/rendering).

This work is available on GitHub - Edito_resampling_datasets .

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Resampling large zarr datasets to lower resolution and creating pyramids in which each level has increasing resolution.

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