Machine Learning & Deep Learning for Remote Sensing and GIS Training Course

Module 1: Introduction to GeoAI, Remote Sensing, and Python Spatial Libraries

ü  Overview of modern Machine Learning and Deep Learning for Remote Sensing and GIS

ü  Fundamentals of geospatial raster/vector data structures and metadata

ü  Setting up the Python GeoAI environment (GDAL, Rasterio, GeoPandas, Xarray)

ü  Principles of applied geospatial AI, environmental analysis, and operational decision‑support

ü  Practical Session: Environment setup and loading, visualizing, and manipulating multi-band satellite rasters in Python

Module 2: Preprocessing Satellite Imagery and Feature Engineering for Machine Learning

ü  Atmospheric, radiometric, and topographic correction techniques

ü  Cloud masking, index calculation (NDVI, NDWI, NDBI), and spectral profiling

ü  Spatial resampling, clipping, and multi-sensor imagery alignment

ü  Feature scaling, normalization, and handling missing spatial data values

ü  Practical Session: Preprocessing Sentinel-2/Landsat imagery and computing spectral indices for ML input features

Module 3: Supervised Machine Learning Algorithms for Land Cover Classification

ü  Random Forest (RF) classification for spatial multi-spectral data

ü  Support Vector Machines (SVM) and Gradient Boosting (XGBoost) for land cover mapping

ü  Sampling strategies, spatial cross-validation, and avoiding spatial autocorrelation bias

ü  Confusion matrix analysis, Kappa coefficient, and accuracy assessment metrics

ü  Practical Session: Building, evaluating, and exporting a Random Forest LCLU classification model

Module 4: Unsupervised Learning and Dimensionality Reduction in GIS

ü  K-Means and DBSCAN clustering for automated pattern discovery

ü  Principal Component Analysis (PCA) for multi-spectral band reduction

ü  Spatial segmentation techniques for Object-Based Image Analysis (OBIA)

ü  Anomaly detection in satellite imagery for environmental monitoring

ü  Practical Session: Applying PCA and K-Means clustering on multi-spectral imagery to detect thermal/spectral anomalies

Module 5: Foundations of Deep Learning and Neural Networks in GeoAI

ü  Artificial Neural Networks (ANN) architecture fundamentals for spatial tabular data

ü  Activation functions, loss metrics, optimizers, and backpropagation mechanics

ü  Introduction to PyTorch and TensorFlow for geospatial applications

ü  Preparing training samples: Patch generation, labeling, and data augmentation techniques

ü  Practical Session: Building a basic Feedforward Neural Network to predict continuous spatial variables

Module 6: Convolutional Neural Networks (CNNs) for Feature Extraction and Object Detection

ü  CNN architectures (U-Net, ResNet, YOLO) tailored for satellite imagery

ü  Semantic segmentation vs. instance segmentation for spatial feature mapping

ü  Automated building footprint extraction and road network detection

ü  Fine-tuning pre-trained vision models using Transfer Learning

ü  Practical Session: Implementing a U-Net model for automated building footprint extraction from high-resolution imagery

Module 7: Advanced Change Detection and Time-Series Remote Sensing Analysis

ü  Multi-temporal imagery processing and image rationing methods

ü  Recurrent Neural Networks (RNN/LSTM) and Transformers for geospatial time-series

ü  Deforestation, urban expansion, and coastal erosion monitoring

ü  Continuous change detection and classification (CCDC) strategies

ü  Practical Session: Executing time-series analysis to measure land cover conversion across a 5-year optical dataset

Module 8: Environmental Analysis and Hazard Risk Modeling Using GeoAI

ü  Machine learning for flood susceptibility mapping and disaster risk assessment

ü  Wildfire burn severity prediction and post-disaster damage estimation

ü  Spatial interpolation and environmental variable modeling (air quality, soil moisture)

ü  Integrating climate model projections into GIS operational pipelines

ü  Practical Session: Developing an ML-driven flood hazard risk model using terrain and hydro-climatic rasters

Module 9: Operational Decision-Support and Model Deployment in GIS Frameworks

ü  Exporting GeoAI models for integration into QGIS, ArcGIS Pro, and Web GIS

ü  Cloud platforms for big geospatial data processing (Google Earth Engine, AWS SageMaker)

ü  Designing operational decision-support dashboards powered by GeoAI outputs

ü  Model governance, reproducibility, and monitoring model drift over time

ü  Practical Session: Packaging a trained deep learning model into an automated GIS workflow script for operational deployment

Module 10: Capstone GeoAI Project: End-to-End Applied Geospatial Workflow

ü  Defining a real-world environmental or operational problem statement

ü  Data acquisition, pipeline construction, feature engineering, and model selection

ü  Model training, validation, hyperparameter tuning, and spatial performance tuning

ü  Synthesizing findings and presenting operational decision-support intelligence

ü  Practical Session: Executing a complete capstone project on a chosen domain case study and presenting the end-to-end pipeline

About Our Trainers

Our trainers are senior spatial data scientists, remote sensing experts, and GeoAI practitioners with extensive global experience in machine learning, deep learning, applied geospatial AI, environmental analysis, and operational decision‑support. They possess advanced academic qualifications alongside years of hands-on industry expertise across government agencies, environmental research organizations, and private geospatial enterprises.

Quality Statement

Phoenix Training Center is committed to providing high-quality, practical, and up-to-date industry training. Our curriculum is rigorously designed to ensure hands-on skill acquisition, utilizing industry-standard software, real-world datasets, and dynamic teaching methodologies that meet international training benchmarks.

Tailor-Made Courses

We understand that every organization has unique challenges and opportunities as well as unique training needs. Phoenix Training Center offers tailor-made courses designed to address specific requirements and challenges faced by your team or organization. Whether you need a customized curriculum, a specific duration, or on-site delivery, we can adapt our expertise to provide a training solution that perfectly aligns with your objectives. We can customize this Course to focus on your industry, specific risk profile, or internal stakeholder dynamics. Contact us to discuss how we can create a bespoke training program that maximizes value and impact for your team. For further inquiries, please contact us on Tel: +254720272325 / +254737296202 or Email training@phoenixtrainingcenter.com.

Admission Criteria

ü  Participants should be reasonably proficient in English.

ü  Applicants must live up to Phoenix Center for Policy, Research and Training admission criteria.

Terms and Conditions

ü  Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

ü  What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

ü  Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

ü  Course Improvement: The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

ü  Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Kindly send an email to the Training Officer on training@phoenixtrainingcenter.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training. Or call us on +254720272325 / +254737296202

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments a week before the training starts (at least 5 to 7 days before the Training start date) to reserve your seat:

ü  Groups of 5 People and Above – Cheque Payments to: Phoenix Center for Policy, Research and Training Limited should be paid in advance, a week before the training starts.

ü  Invoice: We can send a bill directly to you or your company.

ü  Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

ü  Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

ü  Participants may cancel attendance 14 days or more prior to the training commencement date.

ü  No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Accommodation and Airport Pick-up

For physical training attendees, we can assist with recommendations for accommodation near the training venue. Airport pick-up services can also be arranged upon request to ensure a smooth arrival. Please inform us of your travel details in advance if you require these services. For reservations contact the Training Officer on Email: training@phoenixtrainingcenter.com or on Tel: +254720272325 / +254737296202.