Machine Learning for Remote Sensing Image Segmentation and Feature Extraction Training Course

Introduction

The Machine Learning for Remote Sensing Image Segmentation and Feature Extraction Training Course is designed to provide professionals with hands-on expertise in automating land cover classification, object detection, and spatial pattern recognition using machine learning. This advanced course focuses on cutting-edge approaches that combine AI and geospatial science to enhance the analysis of satellite and drone imagery.

Participants will explore supervised and unsupervised classification techniques, convolutional neural networks (CNNs), and advanced segmentation algorithms to extract land use, infrastructure, vegetation, and hydrological features with precision. This course is ideal for analysts seeking to transition from manual interpretation to intelligent, scalable feature extraction from high-resolution imagery.

Target Audience

• GIS and Remote Sensing Specialists
• Environmental and Land Use Analysts
• Urban Planning and Infrastructure Experts
• Agriculture and Forestry Monitoring Officers
• Disaster Risk and Emergency Mapping Experts
• Data Scientists in Earth Observation
• Researchers in Geospatial AI and Machine Learning

Duration

5 Days

Course Objectives

1. Understand the fundamentals of image segmentation and feature extraction
2. Apply supervised and unsupervised classification methods
3. Train machine learning models for remote sensing analysis
4. Extract land use and environmental features from imagery
5. Perform object-based image analysis using segmentation
6. Utilize Python and cloud platforms for scalable processing
7. Evaluate classification accuracy and refine models
8. Develop end-to-end machine learning pipelines for image analysis

Course Content

Module 1: Introduction to Image Segmentation and Feature Extraction

• Concepts of segmentation in remote sensing
• Applications in land cover and object detection
• Overview of traditional vs. machine learning methods
• Importance of spatial and spectral features
• Introduction to training datasets and labeling

Module 2: Supervised and Unsupervised Classification Techniques

• K-means and ISODATA clustering
• Maximum Likelihood and Support Vector Machines
• Random Forest and Decision Trees
• Accuracy assessment and confusion matrices
• Ground truth data integration

Module 3: Machine Learning Algorithms for Remote Sensing

• Fundamentals of machine learning workflows
• Feature selection and dimensionality reduction
• Training and testing classifiers
• Overfitting and cross-validation
• Model performance metrics

Module 4: Object-Based Image Analysis (OBIA)

• Image segmentation algorithms: multiresolution, edge, region growing
• Object classification based on shape, texture, and context
• Working with high-resolution satellite and UAV imagery
• Rule-based classification and semantic grouping
• Mapping impervious surfaces, buildings, and roads

Module 5: Deep Learning for Feature Extraction

• Introduction to Convolutional Neural Networks (CNNs)
• Image tiles and patch extraction for training
• Transfer learning using pre-trained models
• Infrastructure and vegetation detection
• Use cases in agriculture, urban, and environment

Module 6: Cloud-Based Processing with Google Earth Engine

• Introduction to Earth Engine and its ML libraries
• Importing satellite datasets and creating composites
• Classification and regression using GEE tools
• Batch processing and exporting results
• Developing interactive visualizations and apps

Module 7: Python Programming for Image Analysis

• Setting up image processing workflows in Python
• Using scikit-learn, TensorFlow, and OpenCV
• Image preprocessing and feature engineering
• Integrating GIS libraries like rasterio and geopandas
• Automating segmentation pipelines

Module 8: Project-Based Model Development and Deployment

• Define use case and select training imagery
• Label training data and build a model
• Evaluate results and perform refinement
• Deploy the model on new datasets
• Present insights in maps and analytics reports

General remarks

• Customizable courses are available to address the specific needs of your organization.
• The participant must be conversant in English
• Participants who successfully complete this course will receive a certificate of completion from Lenol Development Center.
• The course fee for onsite training includes facilitation training materials, tea break and lunch.
• Accommodation and airport pick up are made upon request
• For any inquiries reach us through info@lenoldevelopmentcenter.com or +254 710 314 746
• Payment should be made to our bank