Geospatial Data Management and Cloud Platforms for the Mining Industry

About Course

This 3 weeks program is designed for professionals in the mining industry who want to learn how to leverage cloud platforms and manage geospatial data for improved efficiency, collaboration, and decision-making.

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Gain an understanding of the benefits and applications of cloud platforms for managing geospatial data in the mining industry.
Learn how to integrate GIS data with popular cloud platforms like AWS, Azure, and Google Cloud.
Explore web mapping and GIS services for data visualization and sharing within the mining organization.
Discover real-time data management techniques for monitoring mining operations and optimizing resource utilization.
Understand the role of big data analytics in analysing large geospatial datasets for informed decision-making.

Course Content

Module 1: The Power of the Cloud: Understanding Cloud Platforms for Geospatial Data

Introduction to cloud computing concepts and its advantages for storing, managing, and accessing geospatial data.

00:00
Exploring popular cloud platforms for geospatial data: Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP).

00:00
Comparing features and functionalities of different cloud platforms for mining applications

00:00
Case Study: Examining how a mining company migrated its GIS data to the cloud and improved accessibility and collaboration.

00:00

Module 2: Bridging the Gap: Integrating GIS Data with Cloud Platforms

Module 3: Mapping the Web: Web Mapping and GIS Services

Module 4: Keeping it Live: Real-Time Data Management for Mining Operations

Module 5: Big Data for Big Decisions: Big Data Analytics for Mining

Module 6: The Future is Now: Trends and Advancements in Geospatial Data Management

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Geospatial Data Management and Cloud Platforms for the Mining Industry

About Course

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Course Content

Module 1: The Power of the Cloud: Understanding Cloud Platforms for Geospatial Data

Introduction to cloud computing concepts and its advantages for storing, managing, and accessing geospatial data.

Exploring popular cloud platforms for geospatial data: Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP).

Comparing features and functionalities of different cloud platforms for mining applications

Case Study: Examining how a mining company migrated its GIS data to the cloud and improved accessibility and collaboration.

Module 2: Bridging the Gap: Integrating GIS Data with Cloud Platforms

Techniques for uploading and managing geospatial data (vector, raster, LiDAR, etc.) within cloud storage solutions.

Utilizing cloud-based GIS services offered by cloud platforms (e.g., AWS Location Services, Azure Maps).

Setting up access controls and security measures for managing sensitive geospatial data in the cloud.

Hands on Training

Module 3: Mapping the Web: Web Mapping and GIS Services

Introduction to web mapping and its role in sharing geospatial information within the mining organization

Creating interactive web maps using cloud-based GIS services and integrating them with internal dashboards and reports

Utilizing open-source web mapping platforms and tools for customizing map visualizations.

Participants create a simple web map using a cloud-based GIS service and share it with others.

Module 4: Keeping it Live: Real-Time Data Management for Mining Operations

Exploring real-time data sources in the mining industry (sensor data, production monitoring, fleet management)

Utilizing cloud platforms for real-time data ingestion, processing, and visualization.

Setting up alerts and notifications based on real-time data for proactive decision-making

Case Study: Analysing how a mining company utilizes real-time data management on the cloud to optimize equipment performance.

Module 5: Big Data for Big Decisions: Big Data Analytics for Mining

Introduction to big data concepts and its relevance for analysing large geospatial datasets in the mining industry

Exploring cloud-based big data analytics tools and platforms for processing and analysing geospatial data (e.g., spatial data mining).

Applications of big data analytics in mining: Exploration target identification, resource characterization, and environmental impact assessment.

Participants explore a cloud-based big data platform and analyse sample geospatial data sets relevant to mining.

Module 6: The Future is Now: Trends and Advancements in Geospatial Data Management

Exploring emerging trends and advancements in geospatial data management and cloud computing

Discussing the role of artificial intelligence (AI) and machine learning (ML) in analysing geospatial data for mining applications.

The future of geospatial data management: Open data, interoperability, and cloud-based collaboration tools

Q&A session and Wrap-Up: Addressing any remaining questions and summarizing key takeaways from the training program

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