Home › Remote sensing › PARACUDA-NG
PARACUDA-NG QGIS 4 compatibility
QGIS 4 ready Active
Yes — PARACUDA-NG supports QGIS 4 (declared max version 4.99.0).
Spectral machine learning: build, validate and apply regression models for spectroscopy and multispectral / hyperspectral imagery, integrated with QGIS raster layers.
About
PARACUDA-NG (PARAmetric CUbe Data Analysis, Next Generation) is a spectral machine-learning toolkit for spectroscopy and multispectral / hyperspectral remote sensing, embedded in QGIS as a native, dockable panel. A 7-step wizard (Data, Configuration, Preprocess, Model, Validate, Execution, Apply) takes you from loading spectral data through spectral resampling to a selectable sensor grid (Sentinel-2, Landsat 5/7/8/9, EnMAP, EMIT, PRISMA, DESIS, PlanetScope, VENuS and more), preprocessing (smoothing, continuum removal, derivatives, absorbance, baseline correction), and training or comparing regression models (PLS-R, SVM, Ridge, Lasso, MLR, Elastic Net, Huber, Gradient Boosting, Gaussian Process, Random Forest, XGBoost) with optional Optuna hyperparameter tuning and cross-validation. A trained model can be applied directly to a loaded QGIS raster layer, writing the prediction back as a new layer with a matching colour map, or to an unknown tabular dataset. Additional tools include compositional (CLR/ALR/ILR) modelling for parts that sum to 100 percent, a label-permutation and spectral-mixing integrity suite, cross-sensor spectral harmonisation (transfer functions), and a data converter for arbitrary instrument layouts. PARACUDA-NG needs a few Python packages QGIS does not ship (scikit-learn, joblib, optuna, xgboost, rasterio, spectral); it installs them into QGIS's Python on first run, or via Tools then Install Python dependencies.
| Author | Sharad Kumar Gupta |
|---|---|
| Downloads | 274 |
| Latest version | 1.0.2 |
| Last updated | 2026-08-11 (65 days ago) |
| Min QGIS | 3.30.0 |
| Max QGIS | 4.99.0 |
| QGIS 4 ready | Yes |
| Maintenance | Active |
| Category | Remote sensing |
| Tags | prisma, remote sensing, multispectral, machine learning, soil, hyperspectral, enmap, xgboost, emit, regression, chemometrics, random forest, spectroscopy, pls, raster, resampling, sentinel-2 |
Homepage · Repository · Issue tracker · Official page
Alternatives in Remote sensing
- Semi-Automatic Classification PluginQGIS 4 ready
The Semi-Automatic Classification Plugin (SCP) allows for the supervised classification of remote sensing images, providing tools for the do
- Google Earth EngineQGIS 4 ready
Integrates QGIS with Google Earth Engine
Raster classification in QGIS with 11 machine learning algorithms, Optuna hyperparameter search, SHAP explanations, class imbalance handling
Related plugins
- EnMAP-Box 3Not QGIS 4 ready
Imaging Spectroscopy and Remote Sensing for QGIS
- PlanX GeoStats LabQGIS 4 ready
PlanX spatial analysis lab for QGIS planning workflows: data preparation, urban pattern scanning, hot spots, centers, exploratory models and
- Agriculture Index CalculatorQGIS 4 ready
Calculate 52 spectral indices from Sentinel-2 and Landsat 8/9 imagery in one click. Includes NDVI, EVI, NDWI, BSI, LAI and 47 more — with cl
- Disaster Raster ClassifierNot QGIS 4 ready
Disaster risk classification (Flood, Flash Flood, Landslide) using Weighted Ensemble ML
- AI Segmentation by TerraLabQGIS 4 ready
AI segmentation in QGIS 3.22 to 4. Draw a zone, name an object and get every building footprint, tree or solar panel as vector polygons, or
- AI Edit by TerraLabQGIS 4 ready
Generative AI image editing for QGIS 3.22 to 4. Show a redevelopment, a planted street, a flood or an orthophoto redrawn as a clean site pla
Maintenance: Active = updated within 1 year, Quiet = 1–3 years, Likely abandoned = over 3 years.