{"name": "SDB Production Suite", "package_name": "sdb_production_suite", "description": "Professional Satellite Derived Bathymetry (SDB) generation tool using Random Forest Regression.", "about": "The SDB Production Suite provides hydrographic-grade satellite-derived bathymetry mapping tools. It integrates ACOLITE-processed Sentinel-2 multispectral reflectance data with surveyed control soundings to generate MLLW-referenced bathymetric grids. Built under strict open-source governance and human-in-the-loop developer practices. Requires external Python libraries: scikit-learn, joblib, rasterio, and pandas.", "homepage": "https://github.com/AlvinMN2024/SDB_Suite", "repository": "https://github.com/AlvinMN2024/SDB_Suite", "tracker": "https://github.com/AlvinMN2024/SDB_Suite/issues", "author": "Alvin M. Natividad", "tags": ["remote sensing", "machine learning", "bathymetry", "hydrography", "sentinel-2", "sdb", "osgeo"], "downloads": 483, "latest_version": "1.0.2", "versions": [{"version": "1.0.2", "experimental": false, "qgis_min": "3.22.0", "qgis_max": "4.99.0", "downloads": 71, "uploaded_by": "alvinmn", "upload_datetime": "2026-10-15T02:35:10.592635"}]}