๐ Rastereasy๏
Rastereasy is a Python library designed for simple and efficient manipulation of georeferenced images (*.tif, *.jp2, *.shp, โฆ).
It aims to streamline geospatial workflows by providing intuitive tools for:
Reading and processing raster and vector files.
Resampling, cropping, reprojecting, stacking, and more.
Creating visualizations (e.g., color composites, interactive spectral plots).
Training and applying classical Machine Learning algorithms.
Performing late fusion of classifications (e.g., Dempster-Shafer theory).
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๐ Getting Started๏
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๐งช Examples Gallery๏
Examples
- Examples of rastereasy use
- Data for notebooks
- 00 Quick Start
- 01 Read And Plot
- 02 Plot Spectral Bands And Get Pixel Values
- 03 Crop Image
- 04 Reprojection
- 05 Resample
- 06 Deal With Numpy Arrays
- 07 Save Images
- 08 Standardization Of Bands
- 09 Select Bands
- 10 Pixel Vs Geo Coord
- 11 Demo Add Bands
- 12 Remove Bands
- 13 Stack Images
- 14 Deal With Bounding Boxes
- 15 Extract From Shp
- 16 Ex Compute Ndvi
- 17 Demo Adapt Bands With Ot
- 18 Ex Kmeans
- 19 Fusion Dempster Shafer 2Hypotheses
- 20 Prepare Snippets Data For Training
- 21 Create Geoimage From Single Tif Bands
๐ ๏ธ API Reference๏
API Reference
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๐ Main Features๏
Easy reading and writing of georeferenced raster and vector data.
Intuitive tools for reprojection, resampling, cropping, and mosaicking.
Support for visualization and spectral analysis.
Integration with scikit-learn for machine learning tasks.
Tools for classification fusion and spatial reasoning.
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