Tutorials

Scikit-eo provides a rich suite of algorithms specifically designed for environmental studies. These include statistical analysis, machine learning, deep learning, data fusion and spatial analysis. Researchers can leverage these tools to explore patterns, relationships, and trends within their datasets, to uncover complex land or forest degradation or mapping and classify the land cover, and generate insightful visualizations, among others tools.

Scikit-eo tutorials notebooks

1. Machine Learning.ipynb
2. Estimated area and uncertainty in Machine Learning.ipynb
3. Calibrating supervised classification in Remote Sensing.ipynb
4. Kmeans classification.ipynb
5. Fusion of radar and optical images.ipynb
6. Spectral Mixture Analysis.ipynb
7. Principal Components Analysis.ipynb
8. Tasseled-Cap Transformation.ipynb
9. Linear trend analysis.ipynb
10. Logistic regression in remote sensing.ipynb
11. Atmospheric Correction.ipynb
12. Plot an satellite image in RGB.ipynb
13. Plot a satellite image histogram.ipynb
14. Deep Learning Classification FullyConnected.ipynb
15. Clipping an image.ipynb

---

Last update: 2023-08-02