Machine Learning For Geospatial Data Analysis Training Course

Introduction

As geospatial data grows exponentially, Machine Learning (ML) for Geospatial Data Analysis is transforming the way we extract insights and make data-driven decisions. This comprehensive training course equips professionals with the skills to leverage machine learning algorithms for spatial data processing, classification, predictive modeling, and pattern detection. Participants will gain hands-on experience in applying Python-based ML frameworks such as Scikit-learn, TensorFlow, and Google Earth Engine to geospatial datasets. Whether working in urban planning, environmental science, remote sensing, or disaster management, this course empowers learners to build intelligent geospatial solutions for real-world applications.

Target Audience

This course is designed for:
✅ GIS & Remote Sensing Professionals – Looking to integrate machine learning into spatial analysis.
✅ Data Scientists & AI Enthusiasts – Exploring geospatial applications of ML algorithms.
✅ Urban Planners & Environmental Scientists – Applying predictive modeling for sustainable planning.
✅ Disaster Management & Climate Change Analysts – Using ML for risk assessment and mitigation.
✅ Students & Researchers – Seeking hands-on experience in geospatial machine learning techniques.

Course Objectives

By the end of this course, participants will:
✔ Understand Machine Learning for Geospatial Data – Learn the fundamentals of ML and how it applies to GIS and remote sensing.
✔ Work with Geospatial ML Frameworks – Use Python libraries like Scikit-learn, TensorFlow, and Google Earth Engine for spatial data analysis.
✔ Perform Supervised & Unsupervised Learning – Implement classification, clustering, and regression models for geospatial applications.
✔ Process Remote Sensing & Satellite Data – Apply ML techniques to classify land cover, detect change, and monitor environmental patterns.
✔ Develop Predictive Geospatial Models – Use AI to forecast spatial trends and future changes in landscapes.
✔ Automate Geospatial Data Processing – Build scripts for large-scale geospatial machine learning tasks.
✔ Create Advanced Geospatial Visualizations – Use Python and GIS tools to interpret and present ML-driven geospatial insights.

This course empowers professionals to harness the power of machine learning for geospatial intelligence, enhance decision-making, and develop cutting-edge GIS solutions using AI-driven analysis.

Duration

10 Days

Course content

This Machine Learning for Geospatial Data Analysis Training Course is designed to equip GIS professionals, data scientists, and remote sensing experts with cutting-edge machine learning (ML) techniques to analyze, classify, and predict geospatial trends. Below are 12 SEO-friendly course modules that provide a structured, hands-on learning experience with real-world applications.

Module 1: Introduction to Machine Learning for Geospatial Data

???? Overview of machine learning and its applications in GIS
???? Supervised vs. unsupervised learning for spatial data
???? Key ML libraries: Scikit-learn, TensorFlow, Google Earth Engine

Module 2: Data Acquisition & Preprocessing for ML

???? Collecting geospatial data from satellite imagery, LiDAR, and IoT sensors
???? Handling missing values, noise reduction, and feature scaling
???? Geospatial data transformation for ML algorithms

Module 3: Exploratory Data Analysis (EDA) & Feature Engineering

???? Identifying spatial patterns and trends in geospatial datasets
???? Feature selection and extraction techniques for ML models
???? Visualizing spatial data distributions with Python

Module 4: Supervised Learning for Geospatial Data

???? Classification and regression models for GIS applications
???? Decision trees, random forests, and support vector machines (SVM)
???? Training and evaluating ML models for geospatial predictions

Module 5: Unsupervised Learning for Spatial Clustering & Anomaly Detection

???? K-means and DBSCAN clustering for geospatial segmentation
???? Identifying spatial anomalies using ML algorithms
???? Applications in urban planning, crime mapping, and environmental monitoring

Module 6: Deep Learning for Remote Sensing & Image Classification

???? Convolutional Neural Networks (CNNs) for satellite image analysis
???? Land cover classification using deep learning
???? Object detection and feature extraction in aerial imagery

Module 7: Time-Series Analysis & Change Detection with ML

???? Temporal analysis of geospatial data using ML models
???? Predicting land use changes and environmental shifts
???? Monitoring urban expansion and deforestation trends

Module 8: Google Earth Engine for Large-Scale Geospatial ML

???? Introduction to cloud-based geospatial analysis
???? Running ML models on massive remote sensing datasets
???? Automated land cover classification with Google Earth Engine

Module 9: Geospatial Predictive Modeling & Risk Assessment

???? Forecasting climate change impacts with ML
???? Predicting natural disasters and hazard mapping
???? AI-driven geospatial risk assessment for decision-making

Module 10: Automating Geospatial Data Processing with Python & ML

???? Writing scripts to automate ML workflows
???? Batch processing satellite images with Python
???? Optimizing ML model performance for large-scale geospatial data

Module 11: Web GIS & AI-Driven Geospatial Visualization

???? Creating interactive ML-based geospatial dashboards
???? Web-based mapping with Python, Leaflet, and Dash
???? AI-powered spatial storytelling and reporting

Module 12: Real-World Applications & Final Project

???? Case studies in agriculture, urban planning, and disaster management
???? Hands-on project: Building an end-to-end geospatial ML solution
???? Best practices for deploying machine learning in GIS

By the end of this course, participants will master machine learning techniques for geospatial data analysis, automate GIS workflows, and develop AI-powered geospatial solutions.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.