Remote Sensing PROCessing

Machine Learning
in Remote Sensing

Interested in learning how to apply machine learning to remote sensing data? Start your journey with RSPROC!

WHAT you will learn

Section 1: Foundations of Remote Sensing and Python Programming

In the first part, you will learn the foundational knowledge and skills necessary for both remote sensing and Python programming.

Module 00: Python (Jupyter Notebook, Python Basics, Python Web Framework – Django)
Module 01: Remote Sensing

Module 02: Google Earth Engine (Google Colab, Google Earth Engine Python API, Machine Learning in Earth Engine)

Section 2: Methods for Learning from Remote Sensing Data

In the next part, you will explore techniques for learning from remote sensing data.

Module 03: Machine Learning (Introduction to Machine Learning, Evaluation Metrics for Machine Learning, Creating Training Dataset for Machine Learning)
Module 04: Parameter Estimation (Naive Bayes classifier, Maximum Likelihood, Parzen window)
Module 05: K Nearest Neighbors
Module 06: Principal Component Analysis

Module 07: Support Vector Machine
Module 08: Decision Tree (Decision Tree, Bagging, Boosting)
Module 09: Random Forest
Module 10: Logistic Regression

Section 3: Advanced Topics and Applications

In the final part, you will delve into advanced topics, which will enable you to acquire more specialized knowledge and skills in the field.

Module 11: Neural Network (Artificial Neural Networks, Deep Neural Networks and Deep Learning, Convolutional Neural Networks, Transfer Learning, Data Augmentation, and more)
Module 12: Clustering

Module 13: Regression
Module 14: Application

Getting Started With Neural Networks

Do you know how this curve can be obtained?

Getting Started With Deep Learning

Are you aware of the significance of convolution and convolutional layers?

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