$ account opengeos
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.
This repository provides the `lidar` Python package, a specialized library for hydrological depression analysis of high-resolution topographic data, particularly LiDAR-derived DEMs. Its core functionality includes DEM smoothing (mean, median, Gaussian filters), sink extraction, and a novel level-set algorithm for delineating nested depression hierarchy, yielding topological and geometric properties (size, volume, depth, perimeter). The package is designed as a reusable library with a CLI (`lidar` command), a PySimpleGUI-based graphical interface, and integration with ArcGIS via a dedicated toolbox. It also includes Jupyter notebooks for tutorial and research workflows, demonstrating the complete analysis pipeline. The project is research-grade, backed by peer-reviewed publications, and follows a modular architecture with separate modules for filtering, filling, slicing, and mounts. However, some dependencies (GDAL, arcpy) and Python version constraints introduce moderate technical drift risk.