Next-Gen Zarr Web Map Visualization

Visualization of Earth science data is crucial for its exploration and understanding. Web browsers, as a universal platform, face the challenge of rendering complex geospatial data swiftly. This led to the creation of pre-generated static map tiles, allowing quick visualization but limiting user control over data representation and imposing storage and update burdens on providers.

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 scale, color map or perform “band math” where multiple variables are combined to produce a new variable. Other drawbacks impact the data provider, such as storage costs and maintaining a pipeline to constantly update or reprocess the tile storage with 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 dynamic client approach. 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 “dynamic 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 Report”. This report includes the tradeoffs, requirements for preprocessing the data and 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.

Looking ahead, the focus is on making NASA datasets more accessible through these innovative approaches. The use of Kerchunk reference files, or virtual Zarr datasets, will play a key role in indexing various archival file formats used by NASA, such as HDF5 and NetCDF4. With the capability of titiler-xarray to handle any xarray-readable data, a wide range of NASA datasets can be visualized without the need for duplicating data. Additionally, the creation of data pyramids will further enhance visualization speed at lower resolutions.