Training Course on Big Geospatial Data + AI for Advanced Analytics

Training Course on Big Geospatial Data + AI for Advanced Analytics

Introduction

Training Course on Big Geospatial Data + AI for Advanced Analytics delves into the transformative convergence of Big Geospatial Data and Artificial Intelligence (AI), empowering professionals with cutting-edge skills for advanced analytics. Participants will explore how to harness vast volumes of location-based information, from satellite imagery to IoT sensor data, and leverage powerful AI algorithms to extract meaningful insights, automate complex processes, and drive data-driven decision-making across diverse sectors. The curriculum emphasizes practical, hands-on application of GeoAI techniques, focusing on real-world problem-solving in areas such as urban planning, environmental monitoring, disaster management, and smart infrastructure.

The demand for professionals proficient in geospatial AI is rapidly growing as organizations seek to unlock the full potential of their spatial data assets. This course provides a comprehensive pathway to mastering the tools and methodologies required to design, implement, and deploy AI-powered geospatial solutions. Through a blend of theoretical foundations and practical exercises, attendees will gain expertise in machine learning for GIS, deep learning for remote sensing, spatial data science, and cloud-based geospatial analytics platforms, preparing them to lead innovation in the evolving landscape of location intelligence.

Course Duration

10 days

Course Objectives

1. Understand the core concepts of GeoAI, its applications, and the synergistic relationship between geospatial data and AI algorithms.
2. Acquire proficiency in handling, pre-processing, and managing large-scale geospatial datasets from diverse sources like satellite imagery, LiDAR, and IoT sensors.
3. Implement various machine learning (ML) algorithms tailored for geospatial analysis and pattern recognition.
4. Utilize deep learning (DL) architectures such as Convolutional Neural Networks (CNNs) for advanced remote sensing applications, including image classification and object detection.
5. Develop and evaluate predictive models to forecast spatial trends, identify anomalies, and optimize resource allocation in various geospatial domains.
6. Understand techniques for real-time processing and analysis of streaming geospatial data using AI-driven tools for enhanced situational awareness.
7. Gain skills in automating time-consuming geospatial data processing and analytical tasks using AI and scripting languages (Python).
8. Create impactful and interactive geospatial visualizations, dashboards, and maps to effectively communicate AI-generated insights.
9. Apply GeoAI techniques for smart city initiatives, urban growth modeling, infrastructure planning, and traffic management.
10. Understand how AI and geospatial data contribute to environmental conservation, climate change analysis, and disaster risk management.
11. Master data fusion techniques to combine and integrate various geospatial data sources for comprehensive AI analysis.
12. Explore ethical considerations, data privacy, and bias mitigation in the application of AI to geospatial data.
13. Learn to deploy and operationalize AI models and geospatial analytics workflows on cloud platforms

Organizational Benefits

• Empower organizations with data-driven insights derived from large and complex geospatial datasets, leading to more informed and strategic decisions.
• Automate labor-intensive geospatial analysis tasks and data processing workflows using AI, significantly reducing operational costs and time.
• Develop accurate predictive models for various applications, from urban development forecasting to environmental change detection, enabling proactive planning and risk mitigation.
• Gain a significant edge by leveraging cutting-edge AI and geospatial technologies to extract unique insights and develop innovative solutions.
• Facilitate more efficient allocation of resources through precise geospatial intelligence, leading to cost savings and improved outcomes in areas like logistics, agriculture, and infrastructure.
• Enable organizations to respond quickly and effectively to dynamic changes and emerging situations by processing and analyzing geospatial data in real-time.
• Foster a culture of innovation by equipping teams with the skills to explore new applications of Big Geospatial Data and AI, leading to the development of novel products and services.

Target Audience

1. GIS Professionals & Analysts.
2. Data Scientists & Analysts.
3. Urban Planners & Civil Engineers.
4. Environmental Scientists & Researchers.
5. Remote Sensing Specialists.
6. Disaster Management & Humanitarian Aid Professionals.
7. IT & Software Developers.
8. Project Managers & Decision-Makers.

Course Outline

Module 1: Introduction to Big Geospatial Data and AI

• Understanding Big Geospatial Data.
• Introduction to Artificial Intelligence.
• The Convergence of Geo and AI
• Key GeoAI Applications.
• Tools and Ecosystem Overview
• Case Study: Analyzing Twitter data with geotags to map public sentiment during a major event, illustrating the volume and velocity of big geospatial data.

Module 2: Geospatial Data Acquisition and Pre-processing

• Data Sources and Formats
• Geospatial Data Acquisition Techniques
• Data Pre-processing and Cleaning.
• Data Storage and Management.
• Feature Engineering for Geospatial Data
• Case Study: Pre-processing Sentinel-2 satellite imagery for land cover classification, including atmospheric correction and mosaic creation.

Module 3: Fundamentals of Python for Geospatial Analysis

• Python for Geospatial.
• Geospatial Libraries in Python.
• Basic Spatial Operations.
• Data Visualization with Matplotlib and Folium.
• Introduction to Jupyter Notebooks.
• Case Study: Using Python and GeoPandas to analyze demographic data geographically, joining census data with administrative boundaries.

Module 4: Introduction to Machine Learning for Geospatial Data

• ML Fundamentals.
• Scikit-learn for Geospatial.
• Feature Selection and Model Training.
• Model Evaluation Metrics.
• Addressing Spatial Autocorrelation.
• Case Study: Predicting housing prices using geospatial features (proximity to amenities, public transport) with regression models.

Module 5: Deep Learning for Image Classification

• Introduction to Neural Networks.
• Convolutional Neural Networks (CNNs).
• Training CNNs for Image Classification.
• Geo-specific CNN Architectures.
• Interpreting CNN Results.
• Case Study: Classifying land use/land cover from high-resolution satellite imagery using a pre-trained CNN.

Module 6: Object Detection in Geospatial Imagery

• Object Detection Concepts
• Single-Shot Detectors (YOLO, SSD).
• Region-Proposal Networks (Faster R-CNN).
• Training and Evaluation for Geospatial Objects.
• Applications in GIS
• Case Study: Detecting changes in infrastructure (e.g., new buildings, road networks) from time-series satellite imagery using YOLO.

Module 7: Advanced Deep Learning for Change Detection

• Change Detection Methodologies
• Deep Learning for Bi-temporal Image Analysis.
• Identifying and Quantifying Changes.
• Time-Series Geospatial Data.
• Deployment Considerations.
• Case Study: Monitoring deforestation in the Amazon rainforest using Sentinel-1 (SAR) and Sentinel-2 (Optical) imagery with deep learning for change detection.

Module 8: Geospatial Time Series Analysis with AI

• Temporal Dimensions in Geospatial Data.
• Recurrent Neural Networks (RNNs) and LSTMs.
• Forecasting Spatial Phenomena.
• Integrating Time and Space.
• Challenges of Spatio-Temporal Data.
• Case Study: Predicting air quality levels in urban areas using time-series sensor data combined with meteorological and traffic data using LSTMs.

Module 9: Cloud-Based Geospatial AI Platforms

• Introduction to Cloud GIS
• Google Earth Engine (GEE) for Big Geospatial Data.
• Leveraging Cloud AI Services.
• Scalable Processing with Cloud Computing.
• Cost Optimization and Best Practice.
• Case Study: Performing large-scale land cover mapping for an entire continent using Google Earth Engine and its built-in ML capabilities.

Module 10: Geospatial Data Visualization and Communication

• Advanced Mapping Techniques
• Interactive Web Maps
• Dashboard Development
• Storytelling with Spatial Data
• Ethical Visualization.
• Case Study: Developing an interactive web dashboard to visualize traffic congestion patterns and AI-predicted hotspots in a city.

Module 11: AI for Smart Cities and Urban Analytics

• Smart City Concepts.
• Urban Growth Modeling.
• Optimizing Urban Services.
• Crime Prediction and Hotspot Analysis.
• Socio-economic Analysis.
• Case Study: Optimizing emergency vehicle dispatch routes in a metropolitan area by integrating real-time traffic data and historical accident locations with AI-driven routing algorithms.

Module 12: AI in Environmental Monitoring and Disaster Management

• Climate Change Impact Assessment
• Biodiversity Conservation
• Disaster Risk Assessment
• Emergency Response Optimization.
• Environmental Policy and Planning.
• Case Study: Assessing wildfire risk by analyzing vegetation density, weather patterns, and historical fire incidents using machine learning models.

Module 13: Ethics, Privacy, and Bias in GeoAI

• Ethical Considerations in AI.
• Geospatial Data Privacy.
• Bias in AI Models.
• Responsible AI Development
• Legal and Regulatory Landscape.
• Case Study: Discussing the ethical implications of using facial recognition on public CCTV footage combined with geospatial tracking for surveillance in urban areas.

Module 14: Project Development and Deployment

• Project Lifecycle.
• Developing a GeoAI Project.
• Containerization (Docker).
• Web Services and APIs.
• Monitoring and Maintenance.
• Case Study: Developing a simple web application that uses a trained GeoAI model to identify urban green spaces from user-uploaded images.

Module 15: Future Trends and Advanced Topics in GeoAI

• Edge Computing for Geospatial
• Digital Twins and GeoAI
• Generative AI for Geospatial Data.
• Explainable AI (XAI) in GeoAI.
• Quantum Computing and Geospatial.
• Case Study: Discussing the potential of digital twin technology in smart city management, integrating real-time sensor data with AI models for predictive maintenance of infrastructure.

Training Methodology

• Interactive Lectures
• Hands-on Labs and Practical Exercises.
• Case Study Analysis.
• Group Discussions and Collaborative Projects.
• Live Demonstrations
• Instructor-Led Q&A Sessions
• Capstone Project
• Continuous Assessment

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.