Next-Gen Zarr Web Map Visualization
Creators: name: Barciauskas, Aimee
name: Harkins, Sean
name: Sarago, Vincent
name: Jones, Max
name: Banihirwe, Anderson
name: Martin, Kata
Publisher: name: Development Seed
Year: 2023
Subjects: Zarr
Earth Science
Dynamic Visualization
Dynamic Tiling
Open Science
Open Data
NASA Earth Data
AGU 2023

description: Plain-language Summary

Visualization of Earth science data is key to exploring and understanding Earth data. Web browsers offer a near-universal platform for exploring this data. However, web browser users expect near instantaneous page rendering. The scale of geospatial data makes it challenging to serve this data “on-demand” as browsers cannot reproject and create image tiles fast enough for a good user experience.

When Zarr gained popularity for large-scale n-dimensional data analysis, users started to call for browser-based visualization, but no tools existed to visualize Zarr in the browser. Now there are 2 options: a dynamic tile server and a direct client.

The authors have contributed to libraries and testing of both approaches and authored a “Zarr Visualization Cookbook”. This cookbook will discuss the tradeoffs, requirements for preprocessing the data and present performance testing results for when those preprocessing steps were taken or not. We hope that readers will be able to reuse lessons learned and recommendations to deliver their Zarr data to users in web browsers and contribute to the wider adoption of this format for large scale environmental data understanding.
description: Visualization of Earth science data is key to exploring and understanding Earth data. Web browsers offer a near-universal platform for exploring Earth data. However, users expect near instantaneous page rendering. The scale of geospatial data makes it challenging to serve this data “on-demand” as browsers cannot reproject and create image tiles fast enough for a good user experience. This challenge led to the development of pre-generated static map tiles.

While pregenerated map tiles make it possible to visualize data quickly, there are drawbacks. The most significant is the data provider chooses how the data will appear. The user has no power to adjust the visualization, such as modifying the color map or new variable combinations. Other drawbacks impact the data provider, such as storage costs and maintaining a pipeline to constantly update or reprocess new and updated data. Next generation approaches give that power to the user, while still giving providers control over the costs.

More recent years have seen the success of the dynamic tiling approach which allows for on-demand map tile creation. This approach has traditionally relied on Cloud-Optimized GeoTIFFs (COGs). When Zarr gained popularity for large-scale n-dimensional data analysis, users started to call for browser-based visualization, but no tools existed to visualize Zarr in the browser.

Now there are 2 options: a dynamic tile server and a direct client. rio_tiler’s XarrayReader supports tile rendering from anything that is xarray-readable. This means a tile server can render tiles from Zarr stores as well as netCDF4/HDF5 and other formats. However, a tile server still requires running a server while the second option, a “direct client”, reads Zarr directly in the browser client and uses WebGL to render map tiles.

The authors have contributed to libraries and testing of both approaches and authored a “Zarr Visualization Cookbook”. This cookbook will discuss the tradeoffs, requirements for preprocessing the data and present performance testing results for when those preprocessing steps were taken or not. We hope that readers will be able to reuse lessons learned and recommendations to deliver their Zarr data to users in web browsers and contribute to the wider adoption of this format for large scale environmental data understanding.