{"name": "satellite_derived_bathymetry_for_qgis", "package_name": "satellite_derived_bathymetry_for_qgis", "description": "A comprehensive and automated toolkit for Satellite-Derived Bathymetry (SDB) using machine learning, featuring integrated ICESat-2 data retrieval.\r\n\r\nThis plugin is deprecated and no longer maintained. Please use \"Bathymetrix-AI\" instead", "about": "---- This Plugin is deprecated not supported anymore. you can try \"Bathymetrix-AI\".---- \r\n\r\nThe SDB Master Workflow is a powerful QGIS tool that automates Satellite-Derived Bathymetry (SDB) processing. It combines preprocessing, algorithm testing, objective ranking of models, and automated reporting. The tool is designed for scientific research and hydrography, following IHO recommendations.\r\nNew Feature: ICESat-2 Integration\r\n\r\nThe toolkit now includes a dedicated module to download and filter ICESat-2 photon data (ATL03/ATL24) via the SlideRule API. This allows users to easily obtain high-precision bathymetry control points for model training and validation.\r\n\r\nWorkflow Stages:\r\n1-Data Retrieval: Automated download of ICESat-2 bathymetry (SlideRule).\r\n2-Pre-processing: Smart water masking (MNDWI/NDWI) and morphological cleaning.\r\n3-Model Training: Algorithm comparison with hyperparameter tuning (Bayesian Optimization).\r\n4-Refinement: Optional median filtering to reduce noise.\r\n5-Evaluation: Statistical scoring (70% R\u00b2, 30% RMSE) and reporting.\r\n\r\nReferences:\r\nIHO Publication B-13: Cookbook for Satellite-Derived Bathymetry (IHO website).\r\nStumpf et al. (2003): Band Ratio Model in Limnology and Oceanography.\r\nSlideRule Earth: Processing of ICESat-2 data (slideruleearth.io).\r\n\r\nAcknowledgements:\r\nDevelopment leveraged AI-assisted tools: Google Gemini Pro & OpenAI ChatGPT for workflow optimization and SlideRule integration.", "homepage": "https://github.com/Nasef2017/Satellite-Derived-Bathymetry-for-QGIS", "repository": "https://github.com/Nasef2017/Satellite-Derived-Bathymetry-for-QGIS", "tracker": "https://github.com/Nasef2017/Satellite-Derived-Bathymetry-for-QGIS/issues", "author": "Mohamed Aly Nasef", "tags": ["remote sensing", "satellite", "lidar", "hydrography", "machine learning", "bathymetry", "automation", "coastal", "sdb", "randomforest", "icesat-2", "sliderule"], "downloads": 1714, "latest_version": "2.1", "versions": [{"version": "2.1", "experimental": false, "qgis_min": "3.22.0", "qgis_max": "3.99.0", "downloads": 696, "uploaded_by": "nasefmaly", "upload_datetime": "2025-12-06T04:02:38.273079"}, {"version": "2", "experimental": false, "qgis_min": "3.22.0", "qgis_max": "3.99.0", "downloads": 255, "uploaded_by": "nasefmaly", "upload_datetime": "2025-11-24T22:47:25.546084"}, {"version": "1.3", "experimental": false, "qgis_min": "3.22.0", "qgis_max": "3.99.0", "downloads": 502, "uploaded_by": "nasefmaly", "upload_datetime": "2025-10-09T06:18:36.376839"}, {"version": "1.0", "experimental": false, "qgis_min": "3.22.0", "qgis_max": "3.99.0", "downloads": 323, "uploaded_by": "nasefmaly", "upload_datetime": "2025-09-21T09:17:40.484050"}]}