supervised

implicity, readability, and a rich ecosystem of libraries. Its versatility allows seamless integration from data preprocessing to model deployment. Key reasons include: Ease of Use: Python's syntax is intuitive, reducing the learning curve. Extensiv

orting the Classified Map Save the final classified image in the desired format (e.g., GeoTIFF) Create thematic maps or reports based on the classification Best Practices & Tips for Effective Supervised Classification Representative Training Data: Select training sites