Certified GeoAI & Remote Sensing Analyst (C-GeoAIRS)

Certified GeoAI & Remote Sensing Analyst (C-GeoAIRS) Certification Program by Tonex

This program develops professionals who can turn Earth Observation data into actionable intelligence using modern GeoAI. You will master EO/IR and SAR fundamentals, build cloud-native geospatial datasets, and design ML workflows that scale from prototype to production. The curriculum emphasizes disciplined data engineering, reliable modeling, and decision-grade analytics for missions that demand accuracy and speed.

Cybersecurity is woven throughout: secure ingestion, access control, provenance, and adversarial robustness for imagery models. You will learn how to safeguard sensitive locations, handle PII in geospatial contexts, and comply with export and data-sharing rules. By the end, you can evaluate trade-offs, quantify uncertainty, and communicate findings to technical and executive stakeholders. The result is a practitioner who can ship trustworthy geospatial pipelines—end to end.

Learning Objectives:

• Explain EO/IR and SAR imaging fundamentals for ML.
• Build and catalog cloud-native geospatial datasets.
• Train and evaluate classifiers, detectors, and change models.
• Run scalable inference and produce decision-ready products.
• Engineer reliable geospatial pipelines and services.
• Apply MLOps for reproducibility, drift, and performance.
• Implement security, privacy, and governance controls.
• Communicate results with clarity and measurable impact.

Audience:

• Cybersecurity Professionals
• GIS and Remote Sensing Analysts
• Data Scientists and ML Engineers
• Intelligence and Defense Analysts
• Emergency and Disaster Response Planners
• Energy, Utilities, and Infrastructure Engineers
• Aviation, Maritime, and Transportation Planners
• Product and Program Managers in Geospatial

Program Modules:

Module 1: EO/IR & SAR Fundamentals

• Sensor physics and imaging geometry
• Radiometric and atmospheric correction
• SAR concepts, speckle, and layover
• Georeferencing and orthorectification
• STAC, COG, and OGC APIs
• Ethical sourcing and licensing of imagery

Module 2: Geospatial Data Engineering & Pipelines

• Multi-source ingestion and synchronization
• Tiling, pyramids, and spatial indexing
• Metadata, lineage, and provenance tracking
• Cloud-native formats: COG, Zarr, Parquet
• Scalable ETL with Dask/Spark/GeoPandas
• Data quality checks and versioning

Module 3: ML for EO/IR and SAR

• CNNs and transformers for pixel/patch tasks
• Land cover, object, and change detection
• Polarimetry and InSAR feature engineering
• Transfer learning with limited labels
• Augmentation and class imbalance handling
• Uncertainty estimation and calibration

Module 4: Fusion, Analytics, and Inference

• EO/IR + SAR + DEM data fusion
• Time-series modeling and trend analysis
• Anomaly and target detection strategies
• Batch and streaming inference patterns
• Accuracy assessment and geostatistics
• Visualization, dashboards, and reporting

Module 5: MLOps & Productionization

• Reproducible training pipelines (CI/CD)
• Model registry and experiment tracking
• Serving via APIs and microservices
• Monitoring drift and model health
• Cost, latency, and scaling trade-offs
• Governance of models and datasets

Module 6: Security, Privacy, and Ethics

• Access control, encryption, zero trust
• Secure sharing and export-control basics
• Adversarial robustness for imagery models
• Supply-chain security and SBOMs
• Watermarking, provenance, and audit trails
• Responsible use and bias mitigation

Exam Domains:

1. Sensor Physics & Imaging Geometry
2. Cloud-Native Geospatial Data Engineering
3. EO/IR & SAR Modeling and Evaluation
4. Operational Intelligence & Decision Analytics
5. Secure Geospatial Systems, Policy, and Governance
6. Reliability, Risk, and Model Assurance

Course Delivery:
The course is delivered through lectures, interactive discussions, case studies, and project-based learning led by Tonex experts in Certified GeoAI & Remote Sensing Analyst (C-GeoAIRS). Participants gain access to curated online resources, readings, and guided exercises with practical tools and datasets.

Assessment and Certification:
Participants are assessed via quizzes, assignments, and a capstone project. Upon successful completion, participants receive the Certified GeoAI & Remote Sensing Analyst (C-GeoAIRS) certificate from Tonex.

Question Types:

• Multiple Choice Questions (MCGs)
• Scenario-based Questions

Passing Criteria:
To pass the Certified GeoAI & Remote Sensing Analyst (C-GeoAIRS) Certification Training exam, candidates must achieve a score of 70% or higher.

Ready to advance your GeoAI career? Enroll now and build secure, production-grade geospatial pipelines. Team enrollments and custom scheduling are available—contact Tonex to tailor this program to your mission.