9. K-Means Landcover Classification in Python | Remote Sensing & GIS Tutorial скачать в хорошем качестве

9. K-Means Landcover Classification in Python | Remote Sensing & GIS Tutorial

In this tutorial, we dive into unsupervised landcover classification using K-Means in Python — no labels, just pure machine learning! Perfect for remote sensing and GIS beginners. What You'll Learn: Load and stack satellite image bands Create a feature matrix (N×B format) Standardize reflectance values Run K-Means clustering to group pixels Visualize a landcover classification map Tips to choose the best k value Previous Tutorials: Tutorial mentioned in the video:    • 3. How to Stack bands to Create Satellite ...   Tutorial    • 6. Binary Landcover Classification (with A...   Tutorial    • 7. Multiclass Landcover Classification in ...   Tutorial    • 8. How to Extract Water with NDWI in Pytho...   Learn More: Scikit-learn K-Means: https://scikit-learn.org/stable/modul... TIP: The most important hyperparameter in K-means is k. Try k=5, k=6, or k=7 and compare the results. If this helped, LIKE, SUBSCRIBE, and SHARE to grow with us. #kmeans #remotesensing #gis #pythontutorial #landcoverclassification #machinelearning #geospatial #unsupervisedlearning #satelliteimagery