2026-10-07T11:28:11Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33366177 2026-09-28T06:04:02Z category_25789 category_26635 category_26629 item_type_3 month_year_09_2026

HydroBound-ML 2026-03_Magnuszew dataset Patryk Wróblewski (24666396) Anna Fryśkowska-Skibniewska (24666415) Computational modelling and simulation in earth sciences Photogrammetry and remote sensing Geospatial information systems and geospatial data modelling Airborne Lidar Bathymetry (ALB) Machine Learning Random Forest Object-Based Image Analysis (OBIA) Water Boundary Delineation Topo-bathymetric scanning Exponential Decomposition HydroBound-ML Dataset <p dir="ltr">This dataset provides the comprehensive source data, model files, and output results developed for the HydroBound-ML framework. The primary remote sensing data was acquired using the Riegl VQ-880-GII topo-bathymetric system integrated with PhaseOne iXM-RS150F optical sensors (RGB & NIR) over a complex fluvial environment (Pilica and Vistula rivers, Poland).</p><p dir="ltr">💻 <b>Software Repository:</b> The complete Python application (HydroBound-ML Assistant v.1.0.7), source code, and documentation are openly available on GitHub: <a href="https://github.com/PWroblewskiWAT/HydroBound-ML" target="_blank" rel="noreferrer">https://github.com/PWroblewskiWAT/HydroBound-ML</a>.</p><p dir="ltr">The repository is structured into three main components to ensure full workflow reproducibility:</p><p dir="ltr"><b>1. INPUT DATA</b></p><ul><li>Ortho RGB imagery (.tif)</li><li>Ortho NIR imagery (.tif)</li><li>NDVI spectral index raster (.tif)</li><li>NDWI spectral index raster (.tif)</li><li>NGRDI spectral index raster (.tif)</li><li>Project metadata file (.json)</li><li>Digital Terrain Model (DTM) with ellipsoidal heights (.tif)</li><li>Area Of Interest (AOI) polygon (.kml)</li></ul><p dir="ltr"><b>2. MODEL FILES (Updated - v2)</b></p><ul><li>HydroBound-ML model v2 weights (<code>HydroBound-ML_v2.joblib</code>)</li><li>HydroBound-ML v2 manual samples database (<code>HydroBound-ML_v2_manual_samples.joblib</code>)</li><li>HydroBound-ML v2 model training metrics (<code>HydroBound-ML_v2_metrics_log.txt</code>) <i>(Note: The cached textures file is intentionally excluded from this repository due to its significant file size of approximately 64GB; it is not required to reconstruct the processing workflow).</i></li></ul><p dir="ltr"><b>3. OUTPUT RESULTS</b></p><ul><li>Final Water Boundary Polygon without grid (<code>..._water_boundary_polygon.geojson</code>)</li><li>Final Water Boundary Polygon with grid (<code>..._water_boundary_polygon_grid.geojson</code>) <i>(Note: These vector results were generated utilizing the updated v2 machine learning model).</i></li></ul><p dir="ltr">These datasets serve as supplementary material to ensure full transparency for the research article: <br>Wróblewski, P.; Fryśkowska-Skibniewska, A. HydroBound-ML: Automated Water-Surface-Mask Generation for Airborne Lidar Bathymetry Processing Using Hybrid Machine Learning and Object-Based Image Analysis. <i>Sensors</i> <b>2026</b>, <i>26</i>, 6065. <a href="https://doi.org/10.3390/s26196065" target="_blank" rel="noreferrer">https://doi.org/10.3390/s26196065</a>.</p><p dir="ltr"><br></p> 2026-09-28T06:04:02Z Dataset Dataset 10.6084/m9.figshare.33366177.v5 https://figshare.com/articles/dataset/_b_HydroBound-ML_b_2026-03_Magnuszew_dataset/33366177 CC BY 4.0