[general]
icon=icon.png
name=Bathymetrix-AI
qgisMinimumVersion=3.22
version=3.2
author=Nasef M. Aly
email=Eng.m.nasef2017@gmail.com
description=An advanced Machine Learning pipeline for Satellite-Derived Bathymetry (SDB). Features ICESat-2 integration, Linear RANSAC filtering, and Spatial Residual Correction.

about=<h3>Bathymetrix-AI: Advanced SDB Modeling</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 integrates physics-based filtering with machine learning to overcome traditional SDB limitations.</p>
    
    <p><b>Core Workflow (The 5-Module System):</b></p>
    <ul>
        <li><b>1. Pre-processing:</b> Automated sun-glint correction (Hedley), robust water masking (Otsu), and feature engineering (Log-Ratios).</li>
        <li><b>2. Data Filtering:</b> Physics-constrained <b>Linear RANSAC</b> to remove outliers from ICESat-2 photons and calculate uncertainty-based weights.</li>
        <li><b>3. Global Modeling:</b> Competitive training of 11 ML algorithms (e.g., ExtraTrees, RF, XGBoost) with hyperparameter optimization.</li>
        <li><b>4. Spatial Refinement:</b> Application of <b>Universal Spatial Residual Correction</b> (Regression-Kriging) to minimize local biases and merge global trends with local accuracy.</li>
        <li><b>5. Rigorous Validation:</b> Automated generation of stratified statistical reports and wMAPE analysis.</li>
    </ul>

    <p><b>Key References:</b><br>
    - <b>Stumpf et al. (2003):</b> Log-Ratio Algorithm for SDB.<br>
    - <b>Hedley et al. (2005):</b> Sunglint Correction using NIR.<br>
    - <b>Fischler & Bolles (1981):</b> RANSAC Algorithm for robust estimation.<br>
    - <b>Otsu (1979):</b> A threshold selection method from gray-level histograms.<br>
    - <b>Breiman (2001):</b> Random Forests machine learning.<br>
    - <b>Parrish et al. (2019):</b> Satellite-derived bathymetry using ICESat-2.</p>

    <p><i>Developed for scientific research and hydrographic applications.</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,lidar,python
category=Raster
changelog=v3.1: Enhancement for phase 04 and phase 02.
plugin_dependencies=scikit-learn,rasterio,scikit-optimize,scipy,pandas,numpy,matplotlib,sliderule,icepyx,geopandas