EnMAP-Box Documentation

The EnMAP-Box is Python plugin for QGIS, to process, visualize and analyze mulit- and hyperspectral remote sensing data.

Fig. 1 Graphical user interface of the EnMAP-Box

Visit our overview on EnMAP-Box features and get going with the Installation and Getting Started chapters. Have a look at the Cookbook for usage examples!

Related Sites

• Environmental Mapping and Analysis Program (EnMAP) https://www.enmap.org

• Source code repository https://github.com/EnMAP-Box/enmap-box>

• HYPERedu on eo-college https://eo-college.org/resource-spectrum/hyperspectral

News

Upcoming Events

Title	Date	Location	Comments
Release of EnMAP-Box 3.17	Oct 2025	QGIS Plugin Repository	This will be the last EnMAP-Box version running on QGIS 3.*
Big Data From Space (BiDS) 2025	29.09. - 03.10. 2025	Riga, Latvia	EnMAP-Box live demo, Oct2, 12:35-12:55, Demo Area

Workshop Multi-Source Remote Sensing for Agriculture

16. Sep 2025

On 10. September, the EnMAP-Box was presented in a Hand-on tutorial on the Multi-Source Remote Sensing for Agriculture: Workshop on Advanced Earth Observation, Machine Learning and Artificial Intelligence in Agricultural Applications. The workshop was organized by the ZALF in Müncheberg, Germany. The participants learned how to use the EnMAP-Box to visualize hyper- and multispectral raster data together with spectral profiles from field measurements.

Living Planet Symposium 2025

09. July 2025

At the Living Planet Symposium 2025 in Vienna, Austria (22–27 July), participants had the opportunity to explore the capabilities of the EnMAP-Box. They engaged with us through a tutorial session and by visiting the latest EnMAP-Box poster: We enjoyed connecting with many EnMAP-Box users in person. It was especially exciting to see the high interest for our brand-new EnMAP-Box stickers!

2nd EnMAP User Workshop

14. April 2025

The 2nd EnMAP User Workshop took place from April 2-4, 2025 in in Munich. Here, the EnMAP-Box was presented, and participants could join an Introduction to the EnMAP-Box workshop.

EnMAP Webinar

31. January 2025

More than 100 participants joined the EnMAP - Webinar on the application and Commercialisation potential in which the EnMAP-Box was demonstrated as well.

Older news …

New tutorial: EnMAP-Box in High Performance Computing environments

January 2024

We just published our newest application tutorial “EnMAP-Box in High Performance Computing (HPC) environments”

The tutorial shows how you can:

• install the EnMAP-Box on Linux servers

• start and use QGIS/EnMAP-Box on HPC systems

• process data using QGIS processing algorithms and processing models, that you may have been previously created with the QGIS Model Designer

• use the SLURM workload manager to schedule, run and monitor processing jobs

Hyperspectral 2024 / WICSIS

November 2024

The EnMAP-Box was presented at the 3rd Workshop on International Cooperation in Spaceborne Imaging Spectroscopy (WICSIS, 13.-15. September) at ESA-ESTEC in Noordwijk, Netherlands. The workshop participants got the opportunity to visit a training day on September 12th, on which they learned how to access EnMAP data and how to use the EnMAP-Box.

Here is the newest EnMAP-Box poster:

QGIS User Conference 2024

September 2024

From September 9-10, the QGIS User Conference 2024 (https://uc2024.qgis.sk/) took place in the beautiful city of Bratislava, Slovakia. The EnMAP box was also presented there with the talk Beyond the NDVI: Hyperspectral remote sensing in QGIS with the EnMAP Box.

13th EARSeL Workshop on Imaging Spectroscopy

April 2024

The EnMAP-Box will be presented at the 13th EARSeL Workshop on Imaging Spectroscopy from 16.-18. April 2024 in València.

Don’t miss:

• What’s New in the EnMAP-Box? Visualization and Analysis of EnMAP Data for Everyone (Wed 17.04. 16:00-17:15, Session 2-11, ADAIT Room 1.1-1.2)

• Towards Informed Default Parametrizations of Machine Learning Algorithms for Biophysical Variable Retrieval in the EnMAP-Box (Wed 17.04. 10:30-12:00, Session 2-4, ADAIT Assembly hall)

• Deep Learning based Semantic Segmentation for EnMAP-Box (Thu 18.04. 12:00-13:00, Poster Session, ADAIT Room 0.1)

• EnMAP-Box Tutorial https://is.earsel.org/workshop/13-IS-Valencia2024/enmap-box/ (Fri 19.04. Image Processing Laboratory (IPL) of the University of Valencia)

FOSSGIS 2024 Hamburg

March 2024

On 23. March the EnMAP-Box was presented at FOSSGIS 2024 in Hamburg, Germany: https://pretalx.com/fossgis2024/talk/RPUBQR/

QGIS UC 2024 and QGIS Open Day

May 2023

Following the QGIS User and Developer Meeting 2023 in s’Hertogenbosh, Netherlands (https://uc2023.qgis.nl/), Kartoza <https://kartoza.com/> asked us if we can repeat our presentation on Imaging spectroscopy data in QGIS: Challenges and Opportunities for the the QGIS Open Day. Here is the recording:

FOSSGIS 2023 Berlin

March 2023

The EnMAP-Box will be present at the FOSSGIS with a demosession: Visualisierung und Analyse von Satellitenbildern mit der EnMAP-Box (https://pretalx.com/fossgis2023/talk/9WAMJ9/)

Looking forward meeting you there!

UPDATE - Here is the recording of the presentation:

EnMAP-Box Version 3.11 released

Oct. 10, 2022

A new EnMAP-Box version has been released. A list of all improvements can be found in the changelog.

EnMAP-Box Version 3.10 released

June 09, 2022

A new EnMAP-Box version has been released. A list of all improvements can be found in the changelog.

EnMAP-Box Version 3.9 released

Oct. 10, 2021

A new EnMAP-Box version has been released. A list of all improvements can be found in the changelog.

EnMAP-Box Workshop 2021

June 07, 2021

The next EnMAP-Box workshop will be held online on 21-23 June, 2021. The workshop aims to demonstrate the current state of the EnMAP-Box by way of presentations, live demos with Q&A and self-paced tutorials. Registration for up to 250 participants is now open at the workshop website.

FOSSGIS 2021 Rapperswil

June 09, 2021

Benjamin Jakimow presented how to use Spectral Libraries in QGIS using the EnMAP-Box (german).

EnMAP-Box Version 3.7 released

Oct. 27, 2020

A new EnMAP-Box version has been released. It includes product readers for EnMAP L1B, L2C and L2A, PRISMA L2D and DESIS L2A images, and a first version of Spectral Math in the QGIS Expression Builder. A list of all improvements can be found in the changelog.

FOSSGIS 2020

Andreas Rabe presented the EnMAP-Box at the FOSSGIS 2020 in Freiburg. See the full live-demo session here (german):

QGIS User Conference 2019

Two Presentations by EnMAP-Box developers Andreas Rabe and Benjamin Jakimow at the QGIS User conference in Coruña

• Earth observation data processing in QGIS with a python API

• EO Time Series Viewer - A plugin to explore Earth Observation Time Series Data in QGIS

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General

• About
• Features
  • GUI
  • Visualization
  • Spectral Libraries
  • Algorithms
  • Applications
• News
  • Upcoming Events
  • Workshop Multi-Source Remote Sensing for Agriculture
  • Living Planet Symposium 2025
  • 2nd EnMAP User Workshop
  • EnMAP Webinar
  • Older news …
    • New tutorial: EnMAP-Box in High Performance Computing environments
    • Hyperspectral 2024 / WICSIS
    • QGIS User Conference 2024
    • 13th EARSeL Workshop on Imaging Spectroscopy
    • FOSSGIS 2024 Hamburg
    • QGIS UC 2024 and QGIS Open Day
    • FOSSGIS 2023 Berlin
    • EnMAP-Box Version 3.11 released
    • EnMAP-Box Version 3.10 released
    • EnMAP-Box Version 3.9 released
    • EnMAP-Box Workshop 2021
    • FOSSGIS 2021 Rapperswil
    • EnMAP-Box Version 3.7 released
    • FOSSGIS 2020
    • QGIS User Conference 2019
• Contribute
  • Funding Support
  • Bug reports & feature requests
  • Provide source code
    • 1. Create a fork on GitHub
    • 2. Clone the forked EnMAP-Box repository
    • 3. Modify the local repository
    • 4. Commit your changes
    • 5. Push changes back to your fork
    • 6. Create a pull request
  • Documentation
  • Licensing
    • Applying License Terms
    • Contributor License Agreements (CLA)
      • Individual Contributor License Agreement (ICLA)
      • Corporate Contributor License Agreement (CCLA)
      • Submitting License Agreements
      • Developer Certificate of Origin (DCO)
  • Tests and Continuous Integration
• FAQ & Troubleshooting
  • Bug report & feedback
  • Contact
  • Citation
  • FAQ
  • Known Issues
• Roadmap
• Glossary
  • Index with all Terms
  • GIS and Remote Sensing
  • Raster Metadata
  • Machine Learning
• Data Access
  • Step 1: Instrument Planning Portal (IPP)
  • Step 2: EOWEB® GeoPortal
  • Step 3: Download data from the FTP Delivery Server

User Section

• Installation
  • 1. Install QGIS
  • 2. Install Python Dependencies
  • 3. Install EnMAP-Box
  • 4. How to include EnMAP-box into your Python project
• Getting Started
  • 1. Launching the EnMAP-Box
  • 2. Loading data
  • 3. First steps in the GUI
  • 4. Use a Processing Algorithm
  • 5. What’s next?
• Cookbook
  • Clip a raster with a vector
  • Clustering (kmeans)
  • Command line (qgis_process)
    • Basics
    • Example 1: Bulk import of L2A scenes
    • Example 2: Classification
  • Filtering
    • Spatial Filter
    • Spectral Filter
  • Generic Filter (Majority)
  • Map Algebra with ImageMath
    • Calculate NDVI
    • Mask raster with vector
  • Transformation (PCA)
  • Wavelength Information
  • Classification
    • Fit a Classifier
    • Predict Classification
    • Accuracy Assessment
  • Graphical Modeler
  • Regression
• User Manual
  • GUI
    • Toolbar
      • Data Sources
      • Maps and Views
      • Map Tools
      • Vector Tools
      • Earth Observation for QGIS (EO4Q)
    • Panels
      • Data Sources
      • Data Views
      • Spectral Profile Sources
      • Processing Toolbox
      • Cursor Location Values
    • Views
      • Map View
        • Linking
        • Crosshair
      • Spectral Library View
        • Collect profiles
          • Adding information
        • Advanced options
          • Create / Modify profiles with the Field Calculator
          • Spectral Processing
        • Visualization settings
          • General profile plot settings
          • Visualizing profiles
          • Colorize spectra by attribute
        • Loading, Saving and Importing / Exporting
          • Load and Save Spectral Libraries
          • Exporting Profiles
          • Importing Profiles
          • Spectral Profile JSON format
      • Text View
      • Attribute Table View
  • Tools
    • Add Product
    • Add Web Map Services (WMS)
    • Band Statistics
    • Bivariate Color Raster Renderer
    • Class Fraction/Probability Renderer and Statistics
    • Classification Statistics
    • CMYK Color Raster Renderer
    • Color Space Explorer
    • Decorrelation Stretch Renderer
    • Enhanced Multiband Color Renderer
    • HSV Color Raster Renderer
    • Image Cube
    • Metadata Viewer
    • Multisource Multiband Color Raster Renderer
    • Raster Layer Styling
    • Raster Source Band Properties Editor
    • Reclassify
    • Scatter Plot
      • Plotting Raster Band vs. Raster Band
      • Plotting Raster Band vs. Vector Field
    • Virtual Raster Builder
  • Applications
    • Agricultural Applications
    • Classification Dataset Manager
      • Create a classification dataset
      • Edit categories and features
      • Split dataset randomly
    • Classification workflow
    • Classification Workflow (advanced)
      • Quick Mapping
      • Detailed Analysis
        • Dataset
        • Classifier
        • Feature Clustering
        • Feature Ranking
        • Model
        • Classification
      • Settings
      • Log
    • Classification Workflow (deprecated)
    • EO Time Series Viewer
    • EnPT (EnMAP Processing Tool)
    • GFZ EnGeoMAP
    • Image Math (deprecated)
    • Raster math
      • Single-line expressions
      • Use raster bands
      • Use vector fields
      • Use raster/vector masks
      • Blockwise vs. monolithic processing
      • Multi-line code fragments
      • Metadata handling
      • Input raster lists
    • Regression Dataset Manager
    • Regression Workflow (deprecated)
    • Spectral Index Creator
  • Earth Observation for QGIS (EO4Q)
    • GEE Time Series Explorer
      • Getting started
    • Location Browser
    • Profile Analytics
    • Raster Band Stacking
    • Sensor Product Import
    • Spectral Index Explorer
  • Example Data
    • Open
    • Layers
    • Full EnMAP Example Scene
  • Processing Algorithms
    • Usage
      • Processing Toolbox
      • Model Designer
      • Python Console
      • Command Line Usage
    • Algorithms
      • Analysis ready data
        • Aggregate ARD raster layers
        • Tile EnMAP L2A product
        • Tile raster layer
      • Auxilliary
        • Build 3D Cube
        • Create default paletted raster renderer
      • Classification
        • Class fraction layer from categorized layer
        • Class separability report
        • Classification layer accuracy and area report (for stratified random sampling)
        • Classification layer accuracy report
        • Classification layer from class probability/fraction layer
        • Classification layer from rendered image
        • Classification workflow
        • Classifier performance report
        • Fit CatBoostClassifier
        • Fit GaussianProcessClassifier
        • Fit generic classifier
        • Fit LGBMClassifier
        • Fit LinearSVC
        • Fit LogisticRegression
        • Fit RandomForestClassifier
        • Fit SpectralAngleMapper
        • Fit SVC (polynomial kernel)
        • Fit SVC (RBF kernel)
        • Fit XGBClassifier
        • Fit XGBRFClassifier
        • Land cover change statistics report
        • Predict class probability layer
        • Predict classification layer
        • Reclassify raster layer
      • Clustering
        • Fit AffinityPropagation
        • Fit Birch
        • Fit KMeans
        • Fit MeanShift
        • Predict (unsupervised) classification layer
      • Convolution, morphology and filtering
        • Spatial convolution Airy Disk filter
        • Spatial convolution Box filter
        • Spatial convolution custom filter
        • Spatial convolution Gaussian filter
        • Spatial convolution Moffat filter
        • Spatial convolution Ricker Wavelet filter
        • Spatial convolution ring filter
        • Spatial convolution Savitsky-Golay filter
        • Spatial convolution Top-Hat filter
        • Spatial convolution Trapezoid filter
        • Spatial Gaussian Gradient Magnitude filter
        • Spatial generic filter
        • Spatial Laplace filter
        • Spatial Maximum filter
        • Spatial Median filter
        • Spatial Minimum filter
        • Spatial morphological Binary Closing filter
        • Spatial morphological Binary Dilation filter
        • Spatial morphological Binary Erosion filter
        • Spatial morphological Binary Fill Holes filter
        • Spatial morphological Binary Opening filter
        • Spatial morphological Binary Propagation filter
        • Spatial morphological Black Top-Hat filter
        • Spatial morphological Gradient filter
        • Spatial morphological Grey Dilation filter
        • Spatial morphological Grey Erosion filter
        • Spatial morphological Grey Opening filter
        • Spatial morphological Laplace filter
        • Spatial morphological White Top-Hat filter
        • Spatial Percentile filter
        • Spatial Prewitt filter
        • Spatial Sobel filter
        • Spectral convolution Box filter
        • Spectral convolution Gaussian filter
        • Spectral convolution Ricker Wavelet filter
        • Spectral convolution Savitsky-Golay filter
        • Spectral convolution Trapezoid filter
      • Dataset creation
        • Create classification dataset (from categorized raster layer and feature raster)
        • Create classification dataset (from categorized spectral library)
        • Create classification dataset (from categorized vector layer and feature raster)
        • Create classification dataset (from categorized vector layer with attribute table)
        • Create classification dataset (from JSON file)
        • Create classification dataset (from Python code)
        • Create classification dataset (from table with categories and feature fields)
        • Create classification dataset (from text files)
        • Create regression dataset (from continuous-valued raster layer and feature raster)
        • Create regression dataset (from continuous-valued spectral library)
        • Create regression dataset (from continuous-valued vector layer and feature raster)
        • Create regression dataset (from continuous-valued vector layer with attribute table)
        • Create regression dataset (from JSON file)
        • Create regression dataset (from Python code)
        • Create regression dataset (from table with target and feature fields)
        • Create regression dataset (from text files)
        • Create regression dataset (SynthMix from classification dataset)
        • Create unsupervised dataset (from feature raster)
        • Create unsupervised dataset (from JSON file)
        • Create unsupervised dataset (from Python code)
        • Create unsupervised dataset (from spectral library)
        • Create unsupervised dataset (from text file)
        • Create unsupervised dataset (from vector layer with attribute table)
        • Export classification/regression dataset (to text files)
        • Merge classification datasets
        • Random samples from classification dataset
        • Random samples from regression dataset
        • Select features from dataset
      • Export data
        • Save spectral library as GeoJSON file
      • Feature selection
        • Classifier feature ranking (permutation importance)
        • Hierarchical feature clustering
      • Import data
        • Import DESIS L1B product
        • Import DESIS L1C product
        • Import DESIS L2A product
        • Import EMIT L2A product
        • Import EnMAP L1B product
        • Import EnMAP L1C product
        • Import EnMAP L2A product
        • Import Landsat L2 product
        • Import Planet Scope L1/L3 product
        • Import PRISMA L1 product
        • Import PRISMA L2B product
        • Import PRISMA L2C product
        • Import PRISMA L2D product
        • Import Sentinel-2 L2A product
        • Import USGS Spectral Library Version 7
      • Masking
        • Apply mask layer to raster layer
        • Create mask raster layer
        • Layer to mask raster layer
      • Raster analysis
        • Aggregate raster layer bands
        • Aggregate raster layers
        • Convex hull and continuum-removal
        • Create spectral indices
        • Raster layer zonal aggregation
        • Raster math
        • Sample raster layer values
        • VRT band math
      • Raster conversion
        • Create RGB image from class probability/fraction layer
        • Save raster layer as
        • Scale/truncate/convert raster layer
        • Translate categorized raster layer
        • Translate raster layer
        • Write ENVI header
      • Raster miscellaneous
        • Cleanup VRT file
        • Create grid
        • Edit raster source band properties
        • Raster layer bounding polygon
        • Stack raster layers
        • Subset raster layer bands
      • Raster projections
        • Geolocate raster layer
      • Regression
        • Fit CatBoostRegressor
        • Fit GaussianProcessRegressor
        • Fit generic regressor
        • Fit KernelRidge
        • Fit LGBMRegressor
        • Fit LinearRegression
        • Fit LinearSVR
        • Fit PLSRegression
        • Fit RandomForestRegressor
        • Fit SVR (polynomial kernel)
        • Fit SVR (RBF kernel)
        • Fit XGBRegressor
        • Fit XGBRFRegressor
        • Predict regression layer
        • Receiver operating characteristic (ROC) and detection error tradeoff (DET) curves
        • Regression layer accuracy report
        • Regression workflow
        • Regressor performance report
        • Spectral Index Optimizer
      • Spectral Library
        • Aggregate Spectral Profiles
        • Create spectral library (from classification dataset)
        • Create spectral library (from regression dataset)
        • Import Spectral Profiles into a vector layer
      • Spectral resampling
        • Spectral resampling (to custom sensor)
        • Spectral resampling (to DESIS)
        • Spectral resampling (to EMIT)
        • Spectral resampling (to EnMAP)
        • Spectral resampling (to Landsat 4/5 TM)
        • Spectral resampling (to Landsat 7 ETM+)
        • Spectral resampling (to Landsat 8/9 OLI)
        • Spectral resampling (to PRISMA)
        • Spectral resampling (to response function library)
        • Spectral resampling (to Sentinel-2A MSI)
        • Spectral resampling (to Sentinel-2B MSI)
        • Spectral resampling (to wavelength)
        • Spectral resampling (to wavelength and FWHM)
      • Transformation
        • Fit FactorAnalysis
        • Fit FastICA
        • Fit FeatureAgglomeration
        • Fit KernelPCA
        • Fit MaxAbsScaler
        • Fit MinMaxScaler
        • Fit Normalizer
        • Fit PCA
        • Fit QuantileTransformer
        • Fit RobustScaler
        • Fit StandardScaler
        • Inverse transform raster layer
        • Transform raster layer
      • Unmixing
        • Regression-based unmixing
      • Vector conversion
        • Rasterize categorized vector layer
        • Rasterize vector layer
      • Vector creation
        • Random points from categorized raster layer
        • Random points from mask raster layer
        • Random points from raster layer value-ranges
  • Metadata Guide
    • Introduction to STAC Metadata
    • Introduction to GDAL Metadata
• Application Tutorials
  • 1. Regression-based unmixing of urban land cover
    • 1.1. Introduction
      • 1.1.1. 1. Background
      • 1.1.2. 2. Content
      • 1.1.3. 3. Requirements
      • 1.1.4. 4. Further Reading
      • 1.1.5. 4. Data
    • 1.2. Exercise A: Urban land cover
      • 1.2.1. 1. Start the EnMAP-Box
      • 1.2.2. 2. Load data
      • 1.2.3. 3. Visualize raster and vector data
      • 1.2.4. 4. Basic navigation tools
    • 1.3. Exercise B: Spectral mixing
      • 1.3.1. 1. Multiple map views
      • 1.3.2. 2. Visualize image spectra
    • 1.4. Exercise C: Urban spectral libraries
      • 1.4.1. 1. Load spectral libraries
      • 1.4.2. 2. Spectral library handling
    • 1.5. Exercise D: Regression-based unmixing
      • 1.5.1. 1. Introduction
      • 1.5.2. 2. Start the application
      • 1.5.3. 3. Inputs and class selection
      • 1.5.4. 4. Regression Algorithm
      • 1.5.5. 5. Mixing parameters
      • 1.5.6. 6. Outputs
      • 1.5.7. 7. Run the application
      • 1.5.8. 8. Visualize the urban land cover fraction map
    • 1.6. Exercise E: Validation of fraction maps
      • 1.6.1. 1. Create reference fraction map
      • 1.6.2. 2. Statistical validation of fraction maps
    • 1.7. Additional Exercises
  • 2. Regression-based mapping of forest aboveground biomass
    • 2.1. Introduction
      • 2.1.1. 1. Background
      • 2.1.2. 2. Content
      • 2.1.3. 3. Requirements
      • 2.1.4. 4. Further reading
      • 2.1.5. 5. Data
    • 2.2. Exercise A: Getting started with the EnMAP-Box
      • 2.2.1. 1. Start the EnMAP-Box
      • 2.2.2. 2. Load data
      • 2.2.3. 3. Visualize raster data
      • 2.2.4. 4. Basic navigation tools
      • 2.2.5. 5. Multiple map views
      • 2.2.6. 6. Visualize vector data
      • 2.2.7. 7. Extract & visualize image spectra
    • 2.3. Exercise B: Regression based mapping of AGB
      • 2.3.1. 1. Use the Regression Dataset Manager for data preparation
      • 2.3.2. 2. Use the Regression Workflow for estimating AGB
      • 2.3.3. 3. Assess the model performance for AGB estimation
      • 2.3.4. 4. Visualize AGB results
      • 2.3.5. Learning Activities
    • 2.4. Exercise C: Compare AGB estimates with the NDVI
      • 2.4.1. 1. Introduction to Raster Math
      • 2.4.2. 2. Calculate NDVI
      • 2.4.3. 3. Create a forest mask
      • 2.4.4. 4. Apply the forest mask to the AGB estimates
      • 2.4.5. 5. Apply the forest mask to the NDVI
      • 2.4.6. 6. Visualize AGB vs. NDVI with the Scatter Plot tool
      • 2.4.7. Learning Activities
    • 2.5. Additional Exercises
  • 3. Ocean Colour analysis with ONNS
    • 3.1. Overview
      • 3.1.1. About
      • 3.1.2. Background
      • 3.1.3. References
    • 3.2. Data
      • 3.2.1. Test data
      • 3.2.2. ONNS output
    • 3.3. Usage
      • 3.3.1. ONNS GUI
    • 3.4. Credits
      • 3.4.1. Development Lead
      • 3.4.2. Contributors
      • 3.4.3. Funding
  • 4. EnGeoMAP 3.2 Manual
    • 4.1. Introduction to EnGeoMAP 3.2
    • 4.2. 1. Data Preparation and Requirements
      • 4.2.1. Hyperspectral Data:
      • 4.2.2. Spectral Library File:
      • 4.2.3. RGB Color Scheme CSV file:
      • 4.2.4. Example / Test Data:
    • 4.3. 2. How to use/run the EnGeoMAP toolbox
    • 4.4. 4. The Results
    • 4.5. 5. Acknowledgements
    • 4.6. 6. Further Reading
    • 4.7. Literature
  • 5. EnSoMap - Tutorial
    • 5.1. Requirements
      • 5.1.1. Software
      • 5.1.2. Data
    • 5.2. Explore and display the airborne imagery
    • 5.3. Calculate soil masks
    • 5.4. Calculate soil properties and visualize soil maps
    • 5.5. Derive quantitative soil maps and analyze the results
    • 5.6. Extract predicted soil properties values
  • 6. Spectral Libraries
    • 6.1. Getting Started
      • 6.1.1. Start the EnMAP-Box
      • 6.1.2. Load the Example Data
      • 6.1.3. Load a Spectral Library from the Example Data
      • 6.1.4. Basic Visualization Steps
    • 6.2. Working with Spectral Libraries
      • 6.2.1. Profiles with Coordinates
      • 6.2.2. Collect profiles from images
      • 6.2.3. Spectral Profile Source Panel
      • 6.2.4. Changing Profile Styles
    • 6.3. Save or reject modifications
    • 6.4. Import Spectral Profiles
      • 6.4.1. Geopackage
      • 6.4.2. ASD Field Spectrometer
      • 6.4.3. Raster Layer
      • 6.4.4. Field Calculator
    • 6.5. Export Spectral Profiles
      • 6.5.1. GeoPackage / GeoJSON
      • 6.5.2. ENVI Spectral Library
  • 7. HPC environments / SLURM
    • 7.1. Introduction
    • 7.2. Installation
    • 7.3. Run EnMAP-Box GUI
    • 7.4. Run Processing Algorithms
    • 7.5. Run Processing Models
    • 7.6. SLURM Workload Manager
    • 7.7. Example: Import EnMAP L2A data
      • 7.7.1. Download EnMAP products
      • 7.7.2. Extract, Import, Queue
      • 7.7.3. Monitor job status
    • 7.8. Notes
      • 7.8.1. QT_QPA_PLATFORM
      • 7.8.2. Inspect a SLURM node
    • 7.9. References
  • 8. Spectral Imaging Deep Learning Mapper (SpecDeepMap): A Tutorial for Semantic Segmentation
    • 8.1. Introduction to SpecDeepMap
    • 8.2. Installation of SpecDeepMap
      • 8.2.1. Install QGIS & SpecDeepMap via Miniforge/Conda (cross-platform)
      • 8.2.2. Install QGIS & SpecDeepMap with GPU Support (Optional & ONLY available via miniforge + conda)
      • 8.2.3. Timeless Recovery Environment with Explicit Python Package Versions of Original Release (CPU/GPU)
    • 8.3. Getting started
      • 8.3.1. SpecDeepMap Menu
      • 8.3.2. Download Example Data
    • 8.4. Raster Splitter
    • 8.5. Dataset Maker
    • 8.6. Deep Learning Trainer
    • 8.7. TensorBoard Visualizer (optional)
    • 8.8. Deep Learning Tester
    • 8.9. Deep Learning Mapper
    • 8.10. Credits
    • 8.11. Github Repository
  • 9. Manual Retrieval of Vegetation Variables using IVVRM
  • 10. SIO and DASF for N Estimation
  • 11. EnPT

Developer Section

• Installation
  • Overview
  • 1. Have Git Installed
  • 2. Clone the EnMAP-Box Repository
  • 3. Setup the QGIS Environment
  • 4. Setup the IDE
  • Additional Tools
  • OSGeo4W for Devs
    • Setup Environment
    • Debug QGIS with Visual Studio
  • References
• EnMAP-Box repository
  • Structure
    • Overview
    • Scripts for Developers
    • Generated Folders
  • Install the EnMAP-Box repository
  • Install / Update EnMAP-Box Testdata
  • Get Updates from other Repositories
• Build and publish the EnMAP-Box
  • Build the EnMAP-Box Plugin
  • Build the EnMAP-Box documentation
  • Publish the EnMAP-Box
• Cookbook
  • 1. Start the EnMAP-Box
  • 2. Data Sources
    • Add Data Sources
    • List Existing Data Sources
    • Remove Data Sources
    • Data Sources are unique
    • Receive data source changes:
  • 3. Dock Windows
  • 4. Map Tools
  • 5. Spatial Points and Extents
  • 6. Map Locations and Spectral Profiles
• Tutorials
  • Create EnMAP-Box Applications
    • 1. Initialize a new EnMAP-Box Application
    • 2. Define an Menu
    • 3. Define QgsProcessingAlgorithms for the EnMAPBoxAlgorithm Provider
    • 4. Create a Graphical User Interface
    • List of environmental variables
    • Further links and sources
      • Git for Beginners
      • PyQGIS
      • Python Code Documentation
  • 1. Implementing Processing Algorithms
    • Overview
    • Use case description
    • Core algorithm implementation
      • Code listing
      • Test environment
      • Code discussion
    • Core algorithm (advanced)
    • Processing Framework integration
      • Code listing
      • Test environment
      • Code discussion
    • EnMAP-Box integration
      • Code listing
      • Test environment
      • Deployment
  • 2. Graphical User Interfaces
    • Prerequisites
    • Tutorial Sections
      • Setup your programming project
      • Part I: Basics
        • 1. Hello World
        • 2. Hello Real World
        • 3. Create new widgets
          • Create the main widget
          • Add child widgets
        • 4. Signals and Slots
          • Connect Signals
          • Create Signals
          • Overload Signals
        • 5. Context Menus
        • 6. Qt Designer
          • Draw a Frontend
          • Build a complex UI
          • Write the backend
          • QGIS and EnMAP-Box Icons
          • Promote widgets
          • React on User Input
      • Part II: EnMAP-Box GUI
        • 1. Your GUI in the EnMAP-Box
        • 2. GUI - EnMAP-Box interaction
      • Part III: Advanced GUI programming
        • 1. Classification Schemes
        • 2. Spectral Libraries
        • 3. Create a Tree Model
        • 4. Discover Qt Resource Icons
• RFC
  • RFC 1: Enhanced GDAL PAM for QGIS Raster Layers
    • Summary
    • Motivation
    • Approach
    • Implementation
  • RFC 2: Spectral Properties
    • Summary
    • Motivation
    • Approach
    • Implementation
  • RFC 3: Temporal Properties
    • Summary
    • Motivation
    • Approach
    • Implementation
  • RFC 4: Band Properties
    • Summary
    • Motivation
    • Approach
    • Implementation
• Documentation Guidelines
  • Writing Guidelines
    • 1. reStructuredText
      • Basic inline markup
      • Numbering
    • 2. Figures & Screenshots
      • Icons
      • Screenshots with explanations
      • Positioning of info boxes and images
    • 3. Links
• EnMAP-Box Icons

Indices and tables

• Index

• Module Index

• Search Page