Learn
Concepts, tutorials and guides from the team that builds this technology every day. Free to read, practical to use.
Sections
Concepts
Plain-language explainers for the ideas behind GeoAI, RAG and AI agents.
38 articlesBrowse →
02Tutorials
Step-by-step, hands-on walkthroughs with code you can run.
7 articlesBrowse →
03Guides
Checklists and frameworks for planning and adopting AI in your organization.
9 articlesBrowse →
04Experiences
Practical lessons for planning, running and keeping AI projects useful.
6 articlesBrowse →
Start here
Learning paths
Not sure where to begin? Follow a path: each is a short, ordered set of articles that builds on the one before.
Learn the basics, work with free imagery and finish with a land cover map you tested honestly.
Best for: Analysts, planners and students new to GIS or satellite data
- 1GIS basics: rasters, vectors and coordinate systemsConcept · 6 min
- 2GeoAI in plain languageConcept · 8 min
- 3Resolution in satellite imagery: GSD, revisit time and bandsConcept · 7 min
- 4Compute NDVI from Sentinel-2 imagery with PythonTutorial · 11 min
- 5Classify land cover with a random forest in PythonTutorial · 13 min
- 6Show your data on a web map with MapLibreTutorial · 12 min
- 7How to scope a GeoAI projectGuide · 6 min
AI can draft the code, the model and the scene. People are still needed to develop, fine-tune and put them in context, and that takes the fundamentals.
Best for: Anyone who wants to understand, check and adapt what AI builds, not just run it
- 1Back to basics: why maths and science still matter when AI can write the codeConcept · 6 min
- 2Vectors, coordinates and transformations: the maths behind every map and 3D sceneConcept · 7 min
- 3The Earth is not a sphere: datums, heights and distance for GeoAIConcept · 6 min
- 4Light, spectra and the physics behind remote sensingConcept · 6 min
- 5Statistics and uncertainty: how to tell whether a map or model is rightConcept · 6 min
- 6How cameras capture 3D: optics, parallax and photogrammetryConcept · 6 min
- 7The human body in VR and mixed reality: perception, latency and comfortConcept · 6 min
- 8How machine learning models learn: loss, gradients and why data mattersConcept · 6 min
- 9Maths in code: distances on the Earth and rotations in 3D with PythonTutorial · 11 min
- 10How to check AI-generated GIS, 3D and machine learning code before you trust itGuide · 6 min
Understand how it works, split documents well, build a pipeline and prove it answers correctly.
Best for: Teams building an AI assistant over their own documents
- 1RAG explained: how AI answers from your own documentsConcept · 7 min
- 2Embeddings in plain language: how AI measures meaningConcept · 6 min
- 3Chunking for RAG: how to split documents so AI can find answersConcept · 6 min
- 4Build a minimal RAG pipeline in PythonTutorial · 12 min
- 5How to evaluate a RAG assistant before you trust itGuide · 6 min
Learn the language and how AI works first, then check your readiness, run one measured pilot and understand the two big building blocks.
Best for: Leaders and small teams deciding where to start
- 1AI basics: the essential terminology, in plain languageConcept · 10 min
- 2How does AI work, and what are the branches of AI?Concept · 10 min
- 3AI readiness checklist for your organizationGuide · 6 min
- 4Your first AI pilot: pick one task and measure it for 30 daysGuide · 6 min
- 5RAG explained: how AI answers from your own documentsConcept · 7 min
- 6AI agents explained: from answering to actingConcept · 7 min
From choosing the right technology to capturing a real place, working with meshes and Gaussian splats, publishing it and training people with it.
Best for: Anyone considering immersive experiences, a digital twin, 3D capture or VR training
- 1VR, AR and mixed reality: which one do you need?Concept · 6 min
- 2Digital twins, explained: a living model of a real placeConcept · 6 min
- 3How cameras capture 3D: optics, parallax and photogrammetryConcept · 6 min
- 4Capturing a real place in 3D: a field checklistGuide · 7 min
- 5Meshes, point clouds, NeRFs and Gaussian splats: how to choose a 3D representationConcept · 6 min
- 6Gaussian splatting explained: photoreal 3D from photos, in real timeConcept · 6 min
- 7Capture a place and turn it into a Gaussian splatTutorial · 7 min
- 83D file formats explained: glTF, OBJ, point clouds and 3D TilesConcept · 6 min
- 9How to optimise 3D models for the web, phones and headsetsGuide · 6 min
- 10Publish a 3D model on the web, with AR on phonesTutorial · 11 min
- 11The human body in VR and mixed reality: perception, latency and comfortConcept · 6 min
- 12How to plan a VR training programme that people actually finishGuide · 7 min
- 13What is new in 3D and spatial computing: a field guide to the recent changesConcept · 6 min
Seven short stories about ratios, averages, probability, geometry, change, growth and optimisation, then the two ideas that power modern AI and 3D.
Best for: Anyone who was told maths is useful and never saw where, from students to managers
- 1The story of the two districts: ratios, percentages and why raw numbers lieConcept · 7 min
- 2The story of the two rivers: averages, spread and the outlier that changes everythingConcept · 7 min
- 3The story of the positive test: probability, base rates and why surprising results are often wrongConcept · 7 min
- 4The story of the tallest tree: geometry, angles and how we measure what we cannot reachConcept · 7 min
- 5The story of the water tank: rates of change, slopes and totals, with no fear of calculusConcept · 7 min
- 6The story of the lily pond: exponential growth, compounding and doublingConcept · 7 min
- 7The story of the delivery rider: optimisation, constraints and finding the best answerConcept · 8 min
- 8Vectors, coordinates and transformations: the maths behind every map and 3D sceneConcept · 7 min
- 9How machine learning models learn: loss, gradients and why data mattersConcept · 6 min
Plain-language decoders for AI, web, software, data, cloud, security, geospatial, immersive and emerging technology vocabulary.
Best for: Managers, founders, students and new team members who nod along in technical meetings
- 1AI basics: the essential terminology, in plain languageConcept · 10 min
- 2How does AI work, and what are the branches of AI?Concept · 10 min
- 3Web and internet jargon decoded: from IP address to APIConcept · 6 min
- 4Software development jargon decoded: repos, builds, releases and sprintsConcept · 6 min
- 5Data jargon decoded: databases, warehouses, pipelines and dashboardsConcept · 6 min
- 6Cloud and infrastructure jargon decoded: servers, containers and serverlessConcept · 6 min
- 7Security and privacy jargon decoded: encryption, authentication and zero trustConcept · 6 min
- 8Geospatial jargon decoded: GIS, GNSS, LiDAR, DEM and the restConcept · 6 min
- 9Immersive tech jargon decoded: XR, passthrough, 6DoF and spatial computingConcept · 7 min
- 10Emerging tech jargon decoded: IoT, 5G, blockchain, edge and quantum without the hypeConcept · 7 min
Library
Browse everything
Showing 1–12 of 54 articles

ConceptBeginner
GIS basics: rasters, vectors and coordinate systems
The three ideas every beginner needs before opening any GIS software, explained without jargon.
GIS Basics · 6 minOpen →

ConceptIntermediate
Chunking for RAG: how to split documents so AI can find answers
Why chunk size changes everything in a document assistant, and a practical way to choose a strategy.
RAG & Agents · 6 minOpen →

ConceptBeginner
Back to basics: why maths and science still matter when AI can write the code
AI can build a map, a model or a 3D scene in minutes. Knowing whether it is right for a real place still takes a person who understands the fundamentals.
Back to Basics · 6 minOpen →

ConceptBeginner
Vectors, coordinates and transformations: the maths behind every map and 3D scene
Points, directions, rotations and scale: the small set of ideas that sits under GIS, game engines, VR headsets and AR.
Back to Basics · 7 minOpen →

ConceptBeginner
Light, spectra and the physics behind remote sensing
Why vegetation looks bright in near-infrared, why shadows look like water and why a model trained on one image can fail on another.
Back to Basics · 6 minOpen →

ConceptIntermediate
The Earth is not a sphere: datums, heights and distance for GeoAI
Why coordinates need a datum, why GPS height is not sea level, and how to measure distances on a curved planet.
Back to Basics · 6 minOpen →

ConceptIntermediate
Statistics and uncertainty: how to tell whether a map or model is right
Accuracy, precision, recall and why a model that is 95 percent accurate can still be useless.
Back to Basics · 6 minOpen →

ConceptIntermediate
How cameras capture 3D: optics, parallax and photogrammetry
From the pinhole camera to drone mapping: how photographs become measurements, and what limits their accuracy.
Back to Basics · 6 minOpen →

ConceptBeginner
The human body in VR and mixed reality: perception, latency and comfort
Why some virtual worlds feel natural and others make people ill, and the science of eyes, balance and timing behind it.
Back to Basics · 6 minOpen →

ConceptIntermediate
How machine learning models learn: loss, gradients and why data matters
A plain-language tour of training, overfitting and generalisation, so you know what a model can and cannot be trusted to do.
Back to Basics · 6 minOpen →

TutorialBeginner
Maths in code: distances on the Earth and rotations in 3D with Python
Put the fundamentals to work: measure real distances, rotate points, convert pixels to map coordinates and see floating-point limits for yourself.
Back to Basics · 11 minOpen →

GuideIntermediate
How to check AI-generated GIS, 3D and machine learning code before you trust it
A practical review routine that catches the quiet mistakes, with the context to give an AI assistant so it gets things right the first time.
Back to Basics · 6 minOpen →
Start a project
Want this applied to your own data?
Learning is the first step. If you would rather have a working system, book a discovery call and we will scope it with you.
