OpenAerialMap (OAM) is a production-grade, full-stack platform for uploading, processing, cataloging, and serving openly licensed aerial imagery, maintained by the Humanitarian OpenStreetMap Team (HOTOSM). The system is composed of multiple microservices: a Litestar-based uploader API with htmx frontend, a FastAPI STAC API backed by pgSTAC, a Go tilepack API for on-demand PMTiles/MBTiles generation, and a React/TypeScript frontend with MapLibre GL. Imagery processing is orchestrated via Argo Workflows (fetch, validate, convert to COG, metadata extraction, STAC registration). The codebase is well-architected with extensive test suites, CI/CD via GitHub Actions, Helm charts for Kubernetes deployment, and architecture decision records (ADRs) documenting key choices.
odc-stac is a Python library that converts STAC (SpatioTemporal Asset Catalog) metadata into the Open Data Cube (ODC) data model, enabling efficient loading of satellite imagery (Sentinel-2, Landsat) into xarray Datasets with Dask support. It defines a pluggable metadata parser (`StacMDParser`) and auxiliary reader (`StacAuxReader`) that integrate with the odc-loader framework, handles STAC extensions for projection, raster, and EO, and provides flexible spatial-temporal binning and grouping. The library also includes a CLI benchmark tool, test utilities for generating synthetic STAC items, and a comprehensive test suite with realistic STAC item fixtures from multiple providers (Planetary Computer, USGS, Geoscience Australia). While primarily an application library, it is used extensively in geospatial data research pipelines. The codebase is well-structured and modular, though it depends on evolving STAC specifications and extensions, introducing moderate technical drift risk.
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.