This release provides a cloud-ready Docker image for Python–R geospatial and earth-science workflows, designed for use in shared JupyterHub and similar multi-user environments. py-rocket-geospatial-2 integrates modern Python and R geospatial stacks with tools commonly used across oceanography, climate science, hydrology, and remote sensing. The image follows design patterns from the Pangeo ecosystem and emphasizes scalable, array-based analysis of large spatiotemporal datasets. What this image provides Python geospatial stack optimized for large datasets, distributed computing, and cloud-native object storage R + RStudio with geospatial packages installed using Rocker Project scripts JupyterLab with both Python and R kernel support Desktop environment (VNC) for GUI-based tools such as QGIS and Panoply VS Code OSS configured for scientific notebooks and Quarto Publishing toolchain including Quarto, JupyterBook, MyST, Pandoc, and TeX Live Helper scripts (pyrocket_scripts, rocker_scripts) to support customization and extension Intended use This image is intended to lower barriers for reproducible, cloud-ready analysis of large earth-system datasets, including workflows that authenticate and access data from NASA Earthdata, NOAA and other major earth-observation archives. Licensing & attribution The resulting container image is released under the Apache-2.0 License. Rocker installation scripts retain their original GPL-2.0-or-later licensing. Attribution is appreciated when this container image is used, adapted, or redistributed. Whats Changed Update py-rocket-base image version in Dockerfile to fix LD_LIBRARY_PATHS bug Add automated Python tests via Jupyter notebooks Add pinned package versions Add actions to create draft GitHub release