Details

• Raster I/O
  • Image mosaicking
  • Writing DataArrays to file
• Passing GDAL configuration options
• Distributed processing
  • Use dask to compute with concurrent workers
  • Increase the complexity of the parallel task
  • Use GeoWombat to write a task to file
  • Use GeoWombat to gather block-level results in parallel
• Data extraction
  • Subsetting rasters
  • Clipping to bounds
  • Extracting data with coordinates
  • Extracting data with point geometry
  • Extracting data with polygon geometry
• Band math
  • Vegetation indices
  • Enhanced Vegetation Index (EVI)
  • Two-band Enhanced Vegetation Index (EVI2)
  • Normalized difference indices
  • Tasseled cap transformations
• User functions
  • User apply
  • User functions as DataArray attributes
  • Applying in-memory GeoWombat functions lazily
• Machine learning
  • Recommended classifiers for remote sensing
  • Setup
  • Nodata handling
  • Supervised classification
  • Unsupervised classification (KMeans)
  • Band-stacked classification
  • Cross-validation and hyperparameter tuning
  • Time-stacked classification with temporal_mode
  • Classification with STAC satellite imagery
  • Save prediction output
• Deep learning classifiers
  • Setup and installation
  • Prepare training labels
  • TabNet
  • L-TAE (Temporal Attention)
  • Comparison with sklearn classifiers
  • TorchGeo segmentation models
  • Notes
  • Save predictions
  • Object detection
• Object detection
  • Setup
  • Public API at a glance
  • Run a pretrained detector
  • Sensor config drives band indices
  • Convert polygon labels to boxes
  • Build a YOLO training dataset
  • End-to-end: build, fine-tune, predict
  • Accuracy assessment
  • Visualize TP / FP / FN
  • QGIS review round-trip
  • Refine boxes to polygons with SAM
  • Choosing a backend
  • See also the notebook
• Moving windows
  • Setup
  • Basic usage: moving mean
  • Comparing window sizes
  • Different statistics
  • Percentile filter
  • Distance-weighted moving window
  • Saving results
  • Notes
• Radiometry
  • BRDF-adjusted surface reflectance
• Co-registration
  • AROSICS method
  • Install AROSICS
  • Co-register an image
• Pipeline tasks
  • Setup a task and visualize the steps
  • Create a new task and add to initial task
  • Execute the task pipeline
• Accessing STAC Catalogs
  • Installation
  • Supported catalogs and collections
  • STAC examples
• External examples
• Time series processes on the GPU
  • Local GPU installation
  • Install Python libraries
  • Basic example
  • Stacking multiple statistics
  • Custom modules
  • Combining custom modules
  • Using the band dictionary
  • Generic vegetation indices with user arguments
  • Load and apply PyTorch models
  • Load and apply Tensorflow/Keras models
  • Generating time series file lists