Training course on Advanced Geospatial Machine Learning and AI for Remote Sensing

Module 1: Foundations of AI and Machine Learning in Remote Sensing
Overview:
Understanding how AI and ML technologies are revolutionizing geospatial intelligence and remote sensing analytics.
Key Focus Areas:
Overview of geospatial AI (GeoAI) concepts
The role of machine learning and deep learning in remote sensing
Key ML algorithms for spatial analysis: SVM, Random Forest, K-Means, and CNNs
Data sources: multispectral, hyperspectral, radar, and LiDAR data
Case studies on AI applications in remote sensing (environment, urban, agriculture)
Learning Outcome:
Participants will gain foundational insight into AI-driven remote sensing frameworks and their applications.

Module 2: Geospatial Data Acquisition, Preprocessing, and Feature Engineering
Overview:
Preparing and optimizing remote sensing datasets for AI and ML applications.
Key Focus Areas:
Accessing and downloading satellite data (Sentinel, Landsat, MODIS, Planet)
Image preprocessing: radiometric, atmospheric, and geometric correction
Cloud masking and noise reduction techniques
Data fusion and feature extraction from multisource imagery
Creating geospatial features for ML modeling
Learning Outcome:
Participants will learn to collect, clean, and preprocess geospatial datasets for machine learning tasks.

Module 3: Machine Learning Models for Geospatial Analysis
Overview:
Applying ML algorithms for land cover classification, prediction, and pattern recognition.
Key Focus Areas:
Supervised and unsupervised learning for spatial classification
Land use/land cover mapping using Random Forest and SVM
Clustering methods for unsupervised terrain and vegetation segmentation
Model training, validation, and accuracy assessment
Case study: ML-based land classification using Google Earth Engine
Learning Outcome:
Participants will be able to implement and evaluate ML models for geospatial classification tasks.

Module 4: Deep Learning for Remote Sensing and Object Detection
Overview:
Leveraging advanced neural networks and computer vision models for high-level spatial analysis.
Key Focus Areas:
Introduction to convolutional neural networks (CNNs) for image interpretation
Semantic segmentation and object detection in satellite imagery
Change detection using deep learning
Implementing deep learning models with TensorFlow and Keras
Case study: Identifying buildings, vehicles, or vegetation using CNN models
Learning Outcome:
Participants will acquire skills to design and implement deep learning models for object and pattern detection in remote sensing data.

Module 5: Geospatial Visualization, Cloud Platforms, and AI Integration
Overview:
Visualizing AI results, deploying geospatial models, and integrating AI workflows into decision-making systems.
Key Focus Areas:
Visualization of geospatial analytics results in QGIS and ArcGIS
Cloud computing for geospatial AI (Google Earth Engine, AWS, Azure)
Model deployment and workflow automation
Communicating insights through interactive geospatial dashboards
Future trends: AI-driven geospatial intelligence and policy implications
Learning Outcome:
Participants will learn to visualize, interpret, and operationalize AI-powered remote sensing insights for practical use cases.
Practical Exercises and Case Studies
Preprocessing multispectral imagery using Python and GEE
Land classification with Random Forest in Google Earth Engine
Object detection using CNNs for urban feature mapping
Change detection analysis for environmental monitoring
Visualization of AI-generated outputs in QGIS and Power BI