[general]
icon=icon.png
name=Bathymetrix-AI
qgisMinimumVersion=3.22
qgisMaximumVersion=4.99
version=5.0
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 4-Phase System):</b></p>
    <ul>
        <li><b>1. Automated Pre-processing:</b> Sun-glint removal (Hedley), <b>new Advanced Water Masking</b> (3-Indices), physics-based feature generation (Log-Ratios), and a <b>Deep Water Filter</b> fully customized for ML algorithms.</li>
        <li><b>2. Robust Altimetry Filtering:</b> Uses <b>different algorithms</b> to clean ICESat-2 (ATL24) data by identifying high-confidence inliers and removing environmental noise.</li>
        <li><b>3. Global Auto-ML Modeling:</b> Competitive benchmarking of <b>11 ML algorithms</b> with Randomized Hyperparameter Optimization to find the optimal global depth function.</li>
        <li><b>4. Spatial Residual Stacking:</b> Enhances accuracy by analyzing prediction residuals and re-training the model with a <b>Stacked Error Surface</b> to correct local biases.</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=v5.0: Added Advanced 3-Indices Water Masking (NDWI, MNDWI, NWI) with adaptive thresholding. Introduced customizable Deep Water Filter to isolate Optically Shallow Water (OSW). Implemented full ML Algorithm Customization with complete hyperparameter control for Auto-ML models.