Module 1: Introduction to Big Data for Geospatial Information

• Defining Big Data: Volume, Velocity, Variety, Veracity, Value
• Challenges of traditional GIS with Big Data
• The convergence of GIS and Big Data
• Key concepts: distributed computing, parallel processing
• Overview of the Big Data ecosystem for geospatial
• Practical session: Exploring examples of large geospatial datasets and discussing their characteristics

Module 2: Big Data Technologies and Architectures

• Introduction to distributed file systems (e.g., HDFS)
• Overview of Hadoop ecosystem components (MapReduce, YARN)
• Introduction to Apache Spark for in-memory processing
• NoSQL databases for geospatial data (e.g., MongoDB, Cassandra)
• Cloud computing architectures for Big Data
• Practical session: Setting up a basic distributed computing environment (e.g., a local Spark instance)

Module 3: Geospatial Big Data Sources and Formats

• Satellite imagery archives (e.g., Sentinel Hub, Google Earth Engine)
• IoT sensor data streams (GPS, environmental sensors)
• Social media data with location tags
• Mobile phone data and CDRs (Call Detail Records)
• OpenStreetMap and crowdsourced geospatial data
• Practical session: Accessing and exploring large public geospatial datasets from various sources

Module 4: Distributed Storage for Geospatial Big Data

• Storing vector data in distributed file systems
• Storing raster data in distributed file systems (e.g., Cloud Optimized GeoTIFF - COG)
• Geospatial indexing for efficient retrieval (e.g., Quadtree, R-tree, Geohash)
• Data partitioning and sharding strategies
• Data lakes and data warehousing for geospatial information
• Practical session: Storing and retrieving large vector and raster datasets in a distributed environment

Module 5: Distributed Processing Frameworks for Geospatial Data

• Understanding MapReduce paradigm for spatial operations
• Introduction to Apache Spark's Resilient Distributed Datasets (RDDs) and DataFrames
• Spark SQL for querying spatial data
• Graph processing with Spark GraphX for network analysis
• Other distributed processing tools (e.g., Dask, Flink)
• Practical session: Performing simple spatial operations (e.g., filtering) using Spark

Module 6: Geospatial Data Ingestion and ETL for Big Data

• Data ingestion pipelines for streaming and batch data
• Extract, Transform, Load (ETL) processes for geospatial data
• Data cleaning, validation, and enrichment for Big Data
• Handling different data formats and schemas
• Tools for data integration (e.g., Apache NiFi, Kafka)
• Practical session: Building a simple ETL pipeline for geospatial data ingestion

Module 7: Spatial Querying and Indexing for Big Data

• Advanced spatial indexing techniques for distributed environments
• Efficient spatial joins and overlays on large datasets
• Proximity queries and nearest neighbor searches
• Range queries and spatial filtering
• Optimizing query performance in big data systems
• Practical session: Performing complex spatial queries on a large dataset using distributed indexing

Module 8: Geospatial Analytics with Apache Spark

• Spatial analysis operations with Spark (e.g., buffering, intersection, union)
• Raster processing with Spark (e.g., zonal statistics, map algebra)
• Geospatial libraries for Spark (e.g., Apache Sedona, GeoSpark)
• Performing large-scale spatial statistics
• Building custom spatial analytical functions in Spark
• Practical session: Conducting a large-scale spatial analysis using Apache Spark and a geospatial library

Module 9: Cloud-Based Big Data Geospatial Platforms

• Google Earth Engine for planetary-scale geospatial analysis
• AWS Big Data services for GIS (e.g., S3, EMR, Athena, Redshift)
• Azure Big Data services for GIS (e.g., Blob Storage, HDInsight, Azure Synapse Analytics)
• Leveraging cloud-native geospatial services
• Cost optimization strategies in cloud environments
• Practical session: Performing a geospatial analysis using Google Earth Engine or a cloud-based Big Data service

Module 10: Machine Learning for Geospatial Big Data

• Introduction to machine learning concepts for spatial data
• Supervised learning: classification (e.g., land cover mapping), regression
• Unsupervised learning: clustering (e.g., hot spot analysis)
• Feature engineering for geospatial machine learning
• Model training, evaluation, and deployment on big data
• Practical session: Applying a machine learning algorithm to classify a large geospatial dataset

Module 11: Deep Learning for Geospatial Imagery

• Introduction to deep learning architectures (CNNs, U-Net)
• Deep learning for image classification and object detection in satellite/aerial imagery
• Semantic segmentation of geospatial imagery
• Training deep learning models on large image datasets
• Transfer learning and pre-trained models for geospatial applications
• Practical session: Implementing a deep learning model for image segmentation on a large imagery dataset

Module 12: Real-time Geospatial Big Data Analytics

• Concepts of real-time data processing for geospatial streams
• Stream processing frameworks (e.g., Apache Kafka, Apache Flink, Spark Streaming)
• Real-time spatial indexing and querying
• Applications: fleet tracking, environmental monitoring, disaster response
• Building real-time geospatial dashboards
• Practical session: Setting up a real-time stream processing pipeline for geospatial events

Module 13: Geospatial Big Data Visualization

• Challenges of visualizing large geospatial datasets
• Techniques for visualizing massive point clouds and raster data
• Interactive web-based visualization tools (e.g., deck.gl, CesiumJS)
• Aggregation and generalization for effective visualization
• Storytelling with geospatial big data visualizations
• Practical session: Creating interactive visualizations of large geospatial datasets

Module 14: Geospatial Big Data Security and Governance

• Data privacy and anonymization techniques for location data
• Data governance frameworks for big geospatial data
• Access control and authentication in distributed systems
• Compliance with data regulations (e.g., GDPR, CCPA)
• Best practices for securing geospatial big data
• Practical session: Discussing security considerations and implementing basic access controls

Module 15: Geospatial Big Data Quality and Uncertainty

• Sources of uncertainty in big geospatial data
• Data quality assessment for large datasets
• Propagating uncertainty through analytical workflows
• Visualizing and communicating uncertainty
• Strategies for improving data quality in big data environments
• Practical session: Analyzing data quality issues in a large geospatial dataset

Module 16: Geospatial Big Data Applications and Case Studies

• Smart cities: urban planning, traffic management, infrastructure monitoring
• Environmental monitoring: climate change, deforestation, pollution
• Disaster management: real-time damage assessment, resource allocation
• Telecommunications: network optimization, coverage analysis
• Retail and marketing: location intelligence, customer analytics
• Practical session: Analyzing a real-world big geospatial data case study and proposing solutions

Module 17: Ethical Considerations and Societal Impact of Geospatial Big Data

• Privacy concerns with location tracking and personal data
• Bias in data and algorithms and its societal implications
• Digital divide and access to geospatial big data
• Responsible use of geospatial intelligence
• Future societal challenges and opportunities
• Practical session: Group discussion on ethical dilemmas in geospatial big data applications

Module 18: Future Trends in Big Data Analytics for Geospatial Information

• Edge computing and distributed intelligence for geospatial
• Blockchain for secure geospatial data sharing
• Quantum computing's potential impact on spatial analytics
• Advancements in AI and autonomous geospatial systems
• The evolving landscape of geospatial big data platforms
• Practical session: Brainstorming future applications and research directions in geospatial big data analytics

Requirements:

·       Participants should be reasonably proficient in English.

·       Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

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

2. 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.

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

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

5. 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

Simply send an email to the Training Officer on training@armstrongglobalinstitute.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 / +254725012095 / +254724452588

Payment Options

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

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

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

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

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

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

3. 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.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588