Training Course on GIS and Cloud Computing with AWS/Azure - AWS, Azure

Module 1: Introduction to Cloud GIS

Theory:

Overview of cloud computing concepts: IaaS, PaaS, SaaS.

Benefits of cloud GIS for scalability, collaboration, and data accessibility.

Comparison of AWS and Azure GIS services.

Hands-on: Explore cloud GIS services and sample datasets.

Case Study: Cloud GIS deployment for municipal planning.

Module 2: Cloud Data Storage and Management

Theory:

Cloud storage options: S3, Blob Storage, and managed databases.

Data formats, metadata, and versioning for geospatial data.

Data migration strategies and pipelines.

Hands-on: Store and organize spatial datasets on AWS S3 and Azure Blob Storage.

Case Study: Managing large satellite imagery collections in the cloud.

Module 3: Cloud-based GIS Databases

Theory:

Cloud GIS databases: PostGIS on RDS, SQL Azure, and NoSQL options.

Data indexing, querying, and optimization in cloud environments.

Integrating relational and non-relational databases with GIS applications.

Hands-on: Deploy and query spatial databases in AWS and Azure.

Case Study: Hosting a geospatial database for urban infrastructure data.

Module 4: Cloud GIS Processing and Analysis

Theory:

Spatial analysis and processing in the cloud.

High-performance computing and parallel processing for GIS.

Integrating cloud APIs with GIS software.

Hands-on: Perform raster and vector analysis using cloud resources.

Case Study: Real-time flood simulation using cloud-based GIS processing.

Module 5: Web GIS Deployment in the Cloud

Theory:

Hosting GIS web applications and services on AWS/Azure.

Setting up ArcGIS Server or open-source alternatives in the cloud.

REST API and web map service configurations.

Hands-on: Deploy a cloud-hosted GIS web application for interactive mapping.

Case Study: Public-facing GIS portal for environmental monitoring.

Module 6: Cloud Integration with IoT and Remote Sensing

Theory:

Streaming IoT sensor data to cloud GIS platforms.

Remote sensing data ingestion and analysis in cloud environments.

Automated workflows for real-time geospatial monitoring.

Hands-on: Integrate IoT sensor feeds into cloud GIS dashboards.

Case Study: Smart city monitoring using cloud GIS and IoT integration.

Module 7: Visualization and Dashboards in Cloud GIS

Theory:

Interactive dashboards and mapping services in the cloud.

Tools: ArcGIS Online, Power BI, and cloud-native visualization services.

Visualizing large-scale geospatial datasets efficiently.

Hands-on: Create a live cloud GIS dashboard with multiple data layers.

Case Study: Monitoring urban infrastructure and environmental parameters.

Module 8: Security, Access, and Collaboration in Cloud GIS

Theory:

User authentication, access control, and data security.

Collaboration features and sharing GIS data securely in the cloud.

Best practices for cloud GIS governance.

Hands-on: Configure roles, permissions, and secure data sharing.

Case Study: Multi-department GIS collaboration in a municipal cloud environment.

Module 9: Optimization and Cost Management

Theory:

Optimizing cloud resources for GIS applications.

Cost monitoring, scaling, and performance tuning.

Cloud resource management strategies for large geospatial projects.

Hands-on: Set up auto-scaling and monitor cloud GIS costs.

Case Study: Optimizing cloud GIS deployment for national satellite imagery analysis.

Module 10: Capstone Project

Project Examples:

Deploy a cloud-based GIS application for smart city management.

Real-time environmental monitoring using cloud GIS and IoT data.

Large-scale satellite imagery analysis and visualization on cloud platforms.

Hands-on: Complete end-to-end workflow: data storage → cloud processing → analysis → visualization → reporting.

Presentation: Demonstrate project outcomes, cloud deployment strategy, and GIS insights.