[general]
icon=icon.png
name=Bathymetrix-AI
qgisMinimumVersion=3.22
qgisMaximumVersion=4.99
version=5.1
author=Mohamed Aly Nasef
email=Eng.m.nasef2017@gmail.com
description=An advanced Machine Learning pipeline for Satellite-Derived Bathymetry (SDB). Features ICESat-2 integration, In-Situ Data filtering, and Spatial Residual Stacking.

about=<h3>Bathymetrix-AI: Advanced SDB Modeling & Spatial Refinement</h3>
    <p><b>Bathymetrix-AI</b> is a specialized QGIS research toolkit designed to derive high-precision bathymetry from corrected multispectral satellite imagery (e.g., Sentinel-2 L2A). It systematically integrates physics-based corrections with data-driven Machine Learning to overcome traditional SDB limitations.</p>
    
    <p><b>Core Workflow (The 5-Phase System):</b></p>
    <ul>
        <li><b>Phase 01: Advanced Pre-processing:</b> Sun-glint correction (Hedley), <b>Advanced Water Masking</b> using 3-Indices (NDWI, MNDWI, NWI), physics-based Log-Ratio features computation, and a <b>Deep Water Filter</b> customized for ML algorithms.</li>
        <li><b>Phase 02: Robust Filtering:</b> Noise removal using Linear RANSAC, LS Variance Fit, or Huber Variance Fit (Zhang et al., 2021).</li>
        <li><b>Phase 03: Global Auto-ML & Feature Analysis:</b> Optional feature correlation analysis, competitive benchmarking of <b>11 ML algorithms</b>, and hyperparameter optimization via Random Search, Grid Search, or Bayesian.</li>
        <li><b>Phase 04: Adaptive Refinement:</b> Spatially localized corrections and residual analysis (Alevizos, 2020) to fix local biases.</li>
        <li><b>Phase 05: Validation & Reporting:</b> Independent accuracy assessment on unseen test data.</li>
    </ul>

    <p><b>Key References:</b><br>
    - <b>Stumpf et al. (2003):</b> Log-Ratio Algorithm for SDB inversion.<br>
    - <b>Hedley et al. (2005):</b> Physics-based sun-glint correction.<br>
    - <b>Fischler & Bolles (1981):</b> RANSAC algorithm for ICESat-2 data filtering.<br>
    - <b>Zhang et al., (2021):</b> LS Variance Fit, or Huber Variance Fit for ICESat-2 data filtering.<br>
    - <b>Alevizos (2020):</b> Residual analysis and spatial refinement in shallow waters.<br>
    - <b>Bergstra & Bengio (2012):</b> Randomized search for hyperparameter optimization.<br>
    - <b>Parrish et al. (2025):</b> Analysis and assessment of global ICESat-2 bathymetry.</p>

    <p><i>Developed for scientific research and hydrographic applications. Creating the Codes and documentation optimized using <b>Google Gemini AI</b>.</i></p>

homepage=https://github.com/Nasef2017/Bathymetrix-AI
repository=https://github.com/Nasef2017/Bathymetrix-AI
tracker=https://github.com/Nasef2017/Bathymetrix-AI/issues

tags=sdb,bathymetry,machine learning,icesat-2,ransac,spatial correction,remote sensing,hydrography,sentinel-2,python
category=Raster
changelog=v5.1: Added Feature Analysis to best select of Bands for ML algorithms. 