Geospatial Analytics, GIS, and Remote Sensing Technologies Ireland

Metadata Management for Remote Sensing Projects Course

Remote sensing metadata management is the systematic process of documenting the lineage, spatial extent, and technical characteristics of Earth observation data. It enables professionals to transform raw satellite imagery into discoverable, interoperable assets that meet international standards for scientific and commercial use. In an era where massive satellite constellations and high-revisit sensors generate petabytes of information, the gap between data acquisition and actionable insight is often a metadata failure.

This course addresses the modern pressure of cloud-native geospatial workflows by bridging traditional standards like ISO 19115 with emerging technologies such as the SpatioTemporal Asset Catalog (STAC) and Cloud Optimized GeoTIFF (COG) architectures. Designed for Earth observation data architects, GIS managers, and geospatial engineers, this program moves beyond theory to provide hands-on experience with metadata harvesters, validation tools, and discovery portals. You will learn to automate the extraction of technical metadata from sensor headers and map it to robust schemas that satisfy both regulatory requirements and internal governance needs. By the end of this training, you will be equipped to implement a metadata lifecycle that ensures your remote sensing projects remain FAIR: Findable, Accessible, Interoperable, and Reusable, ultimately protecting the long-term value of your organization's geospatial investments.

Duration
5 Days
Duration
Certificate
Certificate
Included
Delivery
Instructor-Led
Delivery
Level
Intermediate To Advanced
Level
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Live Online Training

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Mon - Fri (10 Days)
USD 1,700
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Mon - Fri (10 Days)
USD 1,700
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Weekend (8 Wks)
USD 1,700
Starts
Ends
Mon - Fri (10 Days)
USD 1,700
Starts
Ends
Mon - Fri (10 Days)
USD 1,700
Starts
Ends
Weekend (8 Wks)
USD 1,700
Starts
Ends
Mon - Fri (10 Days)
USD 1,700

Classroom Training

In-person sessions at premier locations

Nairobi Kenya
Mon - Fri
5 Days
USD 1,800
Kigali Rwanda
Mon - Fri
5 Days
USD 2,100
Dubai United Arab Emirates (UAE)
Mon - Fri
5 Days
USD 4,600
Abuja Nigeria
Mon - Fri
5 Days
USD 3,100
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In-person training at our premier venues — pick a city and date that works for you.

Location Duration Fee Language
Nairobi, Kenya Mon - Fri (5 Days) USD 1,800 English See dates & reserve →
Kigali, Rwanda Mon - Fri (5 Days) USD 2,100 English See dates & reserve →
Dubai, United Arab Emirates (UAE) Mon - Fri (5 Days) USD 4,600 English See dates & reserve →
Abuja, Nigeria Mon - Fri (5 Days) USD 3,100 English See dates & reserve →
Addis Ababa, Ethiopia Mon - Fri (5 Days) USD 2,400 English See dates & reserve →
Zanzibar, Tanzania Mon - Fri (5 Days) USD 2,900 English See dates & reserve →
Mombasa, Kenya Mon - Fri (5 Days) USD 1,900 English See dates & reserve →
Cape Town, South Africa Mon - Fri (5 Days) USD 4,200 English See dates & reserve →
Johannesburg, South Africa Mon - Fri (5 Days) USD 3,800 English See dates & reserve →
Pretoria, South Africa Mon - Fri (5 Days) USD 3,600 English See dates & reserve →
Kampala, Uganda Mon - Fri (5 Days) USD 2,100 English See dates & reserve →
Lagos, Nigeria Mon - Fri (5 Days) USD 2,500 English See dates & reserve →
Arusha, Tanzania Mon - Fri (5 Days) USD 2,000 English See dates & reserve →
Dar es Salaam, Tanzania Mon - Fri (5 Days) USD 2,094 English See dates & reserve →
Accra, Ghana Mon - Fri (5 Days) USD 3,800 English See dates & reserve →
Nakuru, Kenya Mon - Fri (5 Days) USD 3,200 English See dates & reserve →
Kisumu, Kenya Mon - Fri (5 Days) USD 3,200 English See dates & reserve →
Naivasha, Kenya Mon - Fri (5 Days) USD 1,900 English See dates & reserve →

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About the Course

Organizations today face a critical challenge: the proliferation of dark data within remote sensing archives. Without rigorous remote sensing metadata management, valuable imagery becomes impossible to locate, its provenance becomes suspect, and its utility for temporal analysis vanishes. This course provides a structured framework to solve these operational bottlenecks by focusing on the capabilities you need to demonstrate daily: implementing ISO 19115-1:2014 schemas, automating metadata extraction using GDAL, validating XML records against Schematron rules, and architecting cloud-native catalogs. You will gain hands-on practice in building STAC-compliant repositories while being introduced to the broader ecosystem of the INSPIRE Directive and OGC Catalog Service for the Web (CSW) at an overview level. We turn scattered documentation into a high-performance discovery system that supports both human analysts and automated machine learning pipelines.

The curriculum is specifically designed for professionals who must deliver results under the constraints of limited storage budgets, complex multi-sensor environments, and accelerating regulatory demands for data transparency. You will learn how to navigate the technical nuances of different processing levels, from raw L0 telemetry to orthorectified L3 products, ensuring that every step of the processing chain is documented with precision. This course teaches you to build metadata pipelines using Python-based tools like PySTAC and integrate them into enterprise discovery platforms such as GeoNetwork or CKAN. By focusing on evidence-based documentation and standardized reporting, you will be able to prove the quality and reliability of your geospatial products to stakeholders and external partners alike.


Target Audience

This program is tailored for technical professionals and managers who oversee the lifecycle of satellite, aerial, and drone-based imagery within complex organizational environments.

This course is designed for:

  • Earth Observation Data Architects managing large-scale imagery archives
  • Remote Sensing Scientists responsible for data provenance and lineage
  • Geospatial Data Engineers building automated metadata extraction pipelines
  • GIS Managers overseeing compliance with ISO 19115 standards
  • Satellite Imagery Analysts requiring precise temporal and spatial discovery
  • Environmental Compliance Officers documenting data for regulatory reporting
  • Metadata Specialists focused on geospatial interoperability and OGC standards
  • Cloud Solutions Architects designing cloud-native geospatial data lakes
  • Research Data Managers implementing FAIR principles for Earth sciences
  • Technical Project Leads coordinating multi-sensor remote sensing projects

Course Objectives

The curriculum focuses on the practical application of international standards and modern digital tools to solve real-world data discovery challenges.

By the end of this course, you'll be able to:

  • Assess current metadata maturity using the ISO 19115-1:2014 framework
  • Apply GDAL metadata tools to extract technical sensor parameters
  • Construct valid ISO 19139 XML records for multi-spectral imagery
  • Build a SpatioTemporal Asset Catalog (STAC) for cloud-native discovery
  • Evaluate metadata quality using Schematron validation and XSD schemas
  • Navigate OGC Catalog Service for the Web (CSW) implementation requirements
  • Implement automated metadata harvesting workflows using Python and PySTAC
  • Synthesize processing history into comprehensive lineage and provenance reports

Requirements & Prerequisites

Participants should have a working knowledge of GIS concepts and remote sensing data types (e.g., GeoTIFF, NetCDF). Familiarity with XML or JSON is recommended. Basic Python knowledge is helpful for automation modules but not strictly required for the standards and governance sections.


Professional and Organizational Impact

When you lead remote sensing metadata management with credible data and practical strategies, you become a trusted driver of operational efficiency and data integrity.

As a professional, you will benefit by:

  • Build technical expertise in ISO and OGC geospatial standards
  • Gain confidence in architecting cloud-native geospatial discovery systems
  • Strengthen your ability to automate repetitive data documentation tasks
  • Enhance your professional positioning as a geospatial data authority
  • Develop mastery of modern tools like STAC and GeoNetwork
  • Position yourself for leadership roles in Earth Observation data management
  • Expand your capability to deliver FAIR-compliant geospatial projects

Organizations that embed remote sensing metadata management excellence into their operational context reduce costs, mitigate risks, and build lasting competitive advantage.

Your organization will benefit from:

  • Reduce data discovery time through standardized geospatial cataloging
  • Mitigate risk of data loss or provenance ambiguity
  • Ensure compliance with international geospatial metadata mandates
  • Improve ROI on expensive satellite and aerial data acquisitions
  • Enhance interoperability across cross-functional teams and external partners
  • Strengthen data governance through automated validation and quality checks
  • Position the organization as a leader in open geospatial standards

Training Methodology

This is a practical, outcome-driven course designed to turn remote sensing metadata management aspiration into measurable action and credible reporting.

Methodology includes:

  • Hands-on metadata extraction exercise using GDAL and real satellite headers
  • Scenario simulation requiring metadata decisions for a multi-sensor drone project
  • Metadata audit using an ISO 19115-1 compliance checklist and Schematron
  • Stakeholder mapping exercise for geospatial data discovery and reporting chains
  • Case study analysis from the agriculture, mining, and urban planning sectors
  • Group workshop producing a functional STAC catalog for a sample dataset
  • Reflection exercise benchmarking current organizational practices against FAIR data principles

Upcoming Sessions

Next available dates worldwide

Virtual

(Zoom) Training
USD 1,700
29th Jun-10th Jul 2026

Nairobi

Kenya
USD 2,900
15th Jun-26th Jun 2026

Kigali

Rwanda
USD 3,800
6th Jul-17th Jul 2026

Dubai

United Arab Emirates (UAE)
USD 7,800
15th Jun-26th Jun 2026

Zanzibar

Tanzania
USD 4,300
15th Jun-26th Jun 2026

Addis Ababa

Ethiopia
USD 2,500
27th Jul-7th Aug 2026

Abuja

Nigeria
USD 3,100
27th Jul-31st Jul 2026

Mombasa

Kenya
USD 1,900
15th Jun-19th Jun 2026

Cape Town

South Africa
USD 7,500
15th Jun-26th Jun 2026

Johannesburg

South Africa
USD 3,800
22nd Jun-26th Jun 2026

Kampala

Uganda
USD 3,700
15th Jun-26th Jun 2026

Pretoria

South Africa
USD 5,900
22nd Jun-3rd Jul 2026

Lagos

Nigeria
USD 2,500
15th Jun-19th Jun 2026

Certification

Recognized credentials that advance your career

Participants who complete the Metadata Management for Remote Sensing Projects Program earn a Trainingcred Certificate of Achievement, demonstrating professional competence and alignment with global standards in learning and development.

NITA Accredited

Accredited by the National Industrial Training Authority, ensuring programs meet nationally recognized standards of quality and relevance.

CPD Certified

Recognized by the CPD Certification Service, ensuring every program meets internationally benchmarked standards of professional excellence.

Why this course earns its place on your CV

Accredited training, practitioner trainers, and peers on the same career track — the three things real expertise is built on.

Skills Relevance & Practical Application

  • Master cutting-edge techniques in metadata management for enhanced data accuracy.
  • Transform data chaos into structured success, boosting project efficiency and reliability.
  • Learn to implement metadata standards that scale with evolving remote sensing technologies.

Expert-Led Instruction & Industry Insights

  • Gain insights from leading remote sensing experts with real-world project experience.
  • Benefit from personalized feedback on your metadata strategies from seasoned professionals.
  • Access exclusive case studies that demonstrate successful metadata management practices.

Career Advancement & Professional Credibility

  • Enhance your resume with advanced metadata skills that top employers demand.
  • Earn a certification in Metadata Management, validating your expertise to potential employers.
  • Position yourself as a key player in managing complex datasets for critical decisions.

Industry Tools and Platforms Featured in this Training

The platforms and vendors Ireland teams are running today — taught against real configurations, not generic vendor demos.

1
  • GeoServer Open Source Geospatial Foundation
    Used to publish geospatial layers and expose catalogued data services after metadata has been cleaned and structured.

Real Results from Real Professionals

Thousands of professionals have transformed their careers through our training programs. Now, it's your turn.

IE Built for Ireland

How this course applies where you work

Local laws, real case studies, and data-points that make the curriculum land — not generic global theory.

The Regulations and Standards You’re Accountable To

Regulators, laws, and frameworks governing this discipline in Ireland — and exactly how the curriculum maps to each one.

4

Regulators

  • OSi National mapping authority relevant to coordinate reference information, geospatial reference data, and broader dataset interoperability in Irish mapping workflows.
  • EPA Relevant where remote sensing metadata supports environmental monitoring, land cover analysis, and evidence-based reporting.
  • Marine Institute Relevant for marine and coastal remote sensing projects that require discoverable, well-documented geospatial datasets.
  • DPC Relevant when imagery workflows include personal data, identifiable locations, or governance controls around data handling and retention.

Frameworks the course aligns with

  • 01 Data Protection Act 2018 · 2018
  • 02 Geodetic Survey Act 2003 · 2003
  • 03 Freedom of Information Act 2014 · 2014
  • 04 Environmental Information Regulations 2007 · 2007

Business Results You Can Expect

How participants put this to work the week after training — and the measurable return their organisation can plan for.

How participants apply this

Participants in Ireland typically apply this course when they need to make satellite, drone, or airborne imagery easier to find, validate, and reuse across research or operational teams. They use metadata standards to document lineage, spatial coverage, sensor details, and processing history so datasets can move from raw acquisition into governed catalogues. In practice, that means aligning internal data structures with ISO 19115-style metadata, preparing cloud-native assets for search and interoperability, and reducing the time analysts spend hunting for usable imagery. The course is especially relevant when geospatial teams manage multi-source archives, public-sector mapping projects, or environmental monitoring workflows that depend on traceable data products.

Expected ROI

Within 6–12 months, organisations usually see faster dataset discovery, less duplication of effort, and fewer delays caused by incomplete or inconsistent metadata. Training also improves handoffs between acquisition, processing, and downstream analytics teams because technical metadata is captured more systematically at the point of ingestion. For cloud-native workflows, better metadata quality can shorten the time needed to publish and validate imagery collections for internal users or external stakeholders. The main business value is not a single cost saving figure, but a measurable reduction in rework and a stronger return on existing geospatial data investments.

Frequently Asked Questions

Got questions? We've gathered the answers to common queries to help you feel confident and informed.

Basic familiarity with geospatial data is helpful, but the core methods are focused on documenting and structuring data rather than advanced spatial analysis. Most delegates can apply the concepts first in cataloguing and governance tasks, then deepen into automation and validation workflows.

It shows how to map remote sensing products into metadata structures that work well in searchable, cloud-based catalogs. That matters because discoverability, interoperability, and reproducibility depend on the quality of the metadata attached to each asset.

Yes, because many organisations still need robust formal metadata for governance, interoperability, and long-term preservation. In practice, teams often use ISO-style metadata concepts alongside STAC to support both standards-based compliance and cloud-native discovery.

The main risk is that valuable imagery becomes difficult to locate, trust, or reuse, which leads to reprocessing and duplicated work. Poor lineage and incomplete sensor or spatial information can also undermine downstream analysis and make results harder to defend.

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