Spatial OS — Course Syllabus

Name: Spatial OS
Price: 1497 EUR

Reference syllabus for the Spatial OS course delivered by the 3D Geodata Academy. It defines the learning objectives, audience, technical requirements, the module-by-module program, the assessment scheme, the results indicators and the legal terms of purchase.

"The Spatial AI operating system: scene graphs, LLM agents, multi-modal reasoning and end-to-end facility management apps."

1. Course Overview

Dimension	Details
Format	Self-paced online course delivered through the 3D Geodata Academy LMS.
Price	€1 497 (excl. VAT). See section 7 for the legal payment terms.
Learning Objectives	Build 3D scene graphs: Engineer the data foundation: point cloud processing, multi-modal integration, spatial indexing and graph construction. (M1, M2) Architect LLM-powered agents: Integrate LangChain, scope use cases, plan and reason over 3D scene graphs. (M3, M4) Ship production spatial AI: Deploy Streamlit chat apps with HITL workflows and OpenUSD export. (M5)
Target Audience	Engineers and architects building agentic spatial AI: scene graphs, LLM reasoning over 3D data, and production multi-agent systems.
Prerequisites	Working Python notions help. Watch the prerequisites primer →
Estimated Duration	Approximately 30 hours of focused work. Fully asynchronous.
Access	Direct enrolment via the 3D Geodata Academy. A 14-day legal cooling-off period applies.
Accessibility & Disability	All courses are open to learners with disabilities. A dedicated referent reviews each request to put the right pedagogical and technical adjustments in place. Referent: Dr. Florent Poux — howto@learngeodata.eu.
Contact	Dr. Florent Poux — howto@learngeodata.eu 3D Geodata Academy

A note from Dr. Florent PouxSpatial OS is the layer most teams skip — and it is where the next decade of 3D AI is being built. Stop thinking of LLMs as chatbots; treat them as planners that act on geometry.

2. Technical Stack & Pedagogical Means

Software stack: Python and the standard 3D ecosystem (Open3D, Laspy, NumPy, PyTorch, CloudCompare, Meshroom — adapted to the course focus).
Hardware: any modern laptop (Windows, macOS, Linux). GPU recommended for deep learning modules.
Datasets: reference datasets and notebooks provided.
Infrastructure: proprietary LMS with 24/7 access, automated quizzes, progress tracking and digital course materials.
Modality: alternates theory and hands-on practice — code, real datasets, software use and shippable project deliverables.
Theory resources: video lessons, written handouts, curated research articles and PhD-level references.

3. Course Structure

Module	Title & Focus
M1	3D Data Foundations for AI Setup, foundations, unsupervised systems, HITL.
M2	Graph Intelligence Scene graph construction and VLM enhancement.
M3	Agent Architecture LangChain, planning, file system middleware.
M4	3D Scene Interaction Natural language scene understanding, prompt engineering.
M5	Spatial AI Production Streamlit, streaming, HITL approval, deployment.
M6	Deep Dives (LLMs) Multi-agent orchestration and OpenUSD.
M7	End-to-End Facility Management Capstone integration.

Why this structure, Dr. Florent PouxThe 7 modules are ordered the way I build systems in production, not the way a textbook would list them. If you are new to the topic, follow the sequence. If you are experienced, you can jump ahead, but come back to M7 before the oral defence. That's where everything ties together.

M1 — 3D Data Foundations for AI

Setup, foundations, unsupervised systems, HITL.

Architecture environment and setup.
Data foundations and point cloud engine.
Unsupervised systems and HITL labelling.

M2 — Graph Intelligence

Scene graph construction and VLM enhancement.

Graph theory for 3D.
Scene graph construction.
Relationship extraction.
VLM (Visual Language Model) AI enhancement.
LLM understanding of latent space; latent space experiments.

M3 — Agent Architecture

LangChain, planning, file system middleware.

LLM integration layer (LangChain).
From chat to agents.
Setup, scoping the use case and planning.
File system middleware.

M4 — 3D Scene Interaction

Natural language scene understanding, prompt engineering.

Natural language scene understanding.
Transformation tool and agent state.
Prompt engineering for spatial contexts.

Mid-course checkpoint, Dr. Florent PouxWhen you reach M4 — 3D Scene Interaction, stop and apply what you've learned to a dataset you actually care about. The back half of the course goes faster when the first half sits on a real example, not a toy one.

M5 — Spatial AI Production

Streamlit, streaming, HITL approval, deployment.

Streamlit chat application.
Streaming and verbosity controls.
Point cloud viewer integration.
HITL — human approval workflows.
Deployment to Streamlit Cloud.

M6 — Deep Dives (LLMs)

Multi-agent orchestration and OpenUSD.

Spatial reasoning engine.
Multi-agent orchestration.
Scene manipulation engine.
Intelligent placement and layout.
Real-time interactive interface.
OpenUSD scene graph export.
Performance optimisation, testing, validation.

M7 — End-to-End Facility Management

Capstone integration.

End-to-end facility management application.
Multi-modal spatial reasoning.
Generative 3D AI integration.

Expert tip — Dr. Florent PouxResist the urge to skip Module 2. Without a clean scene graph, your agents will hallucinate spatial relationships that don't exist.

4. Assessment, Certificate & Grading

The OS program tier includes a project deliverable and an oral defence in addition to the quiz-based progression.

Stage	Activity	Validation
Before the course	Positioning quiz.	Informative.
During the course	End-of-module quizzes.	Score ≥ 70 % per quiz.
End of the course	Final quiz.	Score ≥ 80 %.
End of the course	Project deliverable submission.	Reviewed against published rubric.
End of the course	Oral defence with Dr. Florent Poux (45 min).	Score ≥ 15 / 25.

Conditions to obtain the certificate

Validate every end-of-module quiz (≥ 70 %).
Validate the final quiz (≥ 80 %).
Submit the project deliverable and pass the oral defence (≥ 15/25).

Grading scale

Certified — Pass: all criteria validated.
Certified with Merit: oral defence ≥ 18/25.
Certified with Distinction: oral defence ≥ 22/25 and quiz average ≥ 90 %.

Successful learners receive the OS certificate and the Alumni digital badge.

Accessibility & disability: all evaluations can be adapted (extended time, alternative formats, oral or written substitution) on request to the disability referent howto@learngeodata.eu.

5. Course Results & Quality Indicators

3D Geodata Academy publishes its course performance indicators transparently. Figures below cover this course and are updated at the end of each session.

Indicator	Current Result	Target
Number of enrolled learners	Data being consolidated	Continuous growth
Satisfaction rate	Data being consolidated	> 95 %
Success rate (certificate obtained)	Data being consolidated	> 85 %
Drop-out / interruption rate	Data being consolidated	< 5 %
Recommendation rate	Data being consolidated	> 90 %

Indicators consolidated from in-LMS quizzes and end-of-course satisfaction surveys. Last update: April 2026.

6. Next Step

This OS course gives you a complete operational system. To go further with structured mentorship and a wider curriculum, secure your spot below or join the 3D AI Accelerator.

🛒 Secure your spot 🚀 Join the 3D AI Accelerator

The 3D AI Accelerator adds direct mentorship with Dr. Florent Poux, full access to the complete course library (20+ courses), monthly analytics on the 3D spatial AI ecosystem, curated research papers and the private job board with reviews and notes on which roles are worth pursuing.

7. Legal, Accessibility & Purchase Conditions

Digital accessibility

3D Geodata Academy is committed to making its content accessible to learners with disabilities. A dedicated referent oversees content accessibility (captioning, transcripts, screen-reader friendly layouts, alternative evaluation formats). Contact: howto@learngeodata.eu.

Access for learners with disabilities

Every course can be adapted upon request. A short questionnaire at enrolment captures the pedagogical and technical adjustments needed. Disability referent: Dr. Florent Poux — howto@learngeodata.eu.

Payment terms — IP-protection caution & 14-day retraction

Because course content is delivered digitally and immediately accessible, the purchase is structured as follows to protect the intellectual property while preserving your legal right of retraction:

At checkout, the payment is collected as a caution / security deposit for IP infringement protection. Access to the LMS is granted immediately.
The deposit is held for 14 calendar days, matching the legal cooling-off period for distance contracts.
If you exercise your right of retraction within those 14 days and have not consumed a substantial part of the content, the deposit is refunded in full.
At the expiry of the 14-day period, in the absence of a retraction request, the deposit is automatically converted into the actual payment.

By purchasing, the learner acknowledges that the course materials are original works protected by copyright. Any reproduction, redistribution or commercial reuse without prior written consent is prohibited.

Data protection (GDPR)

Personal data is processed for the sole purpose of delivering the course, tracking progress, issuing the certificate and providing customer support. Access, rectification or deletion: howto@learngeodata.eu.

General terms of sale

Full general terms of sale are available on request and joined to every order confirmation.