This repository serves as a curated, analysis-ready catalog of Maxar Open Data satellite imagery for disaster response events (earthquakes, floods, hurricanes, wildfires, etc.). The core output is a structured dataset of metadata files—GeoJSON footprints, MosaicJSON tile indices, and TSV tabular indexes—organized by event in the `datasets/` directory. These files are derived from the official Maxar STAC catalog and provide direct S3 URLs to visual, multispectral, panchromatic, and data-mask assets, enabling rapid discovery and download of high-resolution imagery for damage assessment, change detection, and machine learning. The repository is hybrid in nature: while its primary purpose is data publishing and research, it includes application components such as a Streamlit web app (`streamlit_app.py`) for interactive footprint visualization and a Python script (`maxar_data_catalog.py`) that automates the catalog generation pipeline. GitHub Actions workflows (`maxar_data_catalog.yml`, `deploy.yml`) run daily to refresh the catalog and deploy the static data to GitHub Pages. The data schema follows a consistent STAC-like pattern with fields for acquisition time, platform, GSD, view angles, cloud cover, and quadkey-based tiling, though the metadata version (0.0.1) indicates an early-stage format. The catalog is designed for geospatial analysts, remote sensing researchers, and disaster response teams who need quick access to pre- and post-event satellite imagery. The included Jupyter notebook (`examples/maxar_open_data.ipynb`) demonstrates how to load and visualize the catalog using leafmap and geopandas, providing a reusable template for exploration. The repository's value lies in its standardized, openly accessible metadata index rather than in complex software engineering.