$ show opengeos/maxar-open-data
This repository curates and distributes Maxar Open Data satellite imagery for disaster response events. It provides a structured catalog of pre- and post-event high-resolution imagery (WorldView, GeoEye) in multiple formats: GeoJSON footprints, union geometries, MosaicJSON tile indices, and TSV metadata tables. The core pipeline (`maxar_data_catalog.py`) fetches the official Maxar STAC catalog, iterates over event collections, and generates these derived files, which are then committed to the repository. A Streamlit web app (`streamlit_app.py`) offers interactive browsing of footprints and mosaics. The repository is primarily a data asset with a supporting ETL pipeline and a thin frontend, making it a hybrid of data curation and a lightweight application. The data is organized by disaster event (e.g., hurricanes, earthquakes, floods) and includes rich acquisition metadata (platform, GSD, cloud cover, viewing angles) suitable for geospatial analysis and machine learning.
The LLM identified 1 files worth highlighting for reuse:
The primary ETL script that ingests the Maxar Open Data STAC catalog, enumerates event collections, and generates per-catalog GeoJSON footprints, union geometries, MosaicJSON tile indices, and TSV metadata files. It uses leafmap for STAC item retrieval and geopandas for spatial operations. This component is reusable as a template for building similar satellite imagery catalogs from any STAC-compliant source, with minimal adaptation needed to change the source URL or output format.