Where and how can geo-embeddings be used?
From traditional workflows to embedding-driven practice
Aim & Objectives
Aim
- Understanding how geo-embeddings transform geospatial workflows.
Objectives
- Explain what geo-embeddings represent
- Compare traditional and embedding-based workflows
- Use embeddings for exploration and prediction
- Critically assess strengths and limitations
Why embeddings matter for geo-practitioners
- Reduce feature engineering
- Enable reuse across regions
- Work with limited labels
- Faster experimentation
Why now?
- Rapid growth in EO data
- Availability of pretrained models
- Embeddings shared as datasets
Running example
Land cover / land use classification
- Satellite imagery input
- Widely used, conceptually difficult
- Representative of many geo tasks
Traditional LULC workflow
- Data selection
- Pre-processing
- Feature engineering
- Label collection
- Model training
- Post-processing
Where traditional workflows struggle
- Manual feature design
- Label dependency
- Poor transferability
- Long setup times
Recap: What geo-embeddings are
- Pretrained image representations
- Fixed-length semantic vectors
- Consistent feature space
What geo-embeddings are not
- Not physical measurements
- Not directly interpretable
- Not task-specific
Embedding-based workflow
- Imagery → embeddings
- Minimal preprocessing
- Learning in embedding space
- Interpretation & validation
Comparing workflows
- Handcrafted features vs learned representations
- Task-specific vs reusable
- Region-bound vs transferable
Application 1: Unsupervised classification
- Cluster places by similarity
- No labels required upfront
- Discover structure first
Why unsupervised matters in practice
- Handles ambiguity
- Reveals spatial gradients
- Supports exploratory analysis
Example outcomes
- Place typologies
- Regional patterns
- Unexpected groupings
Application 2: Predictive models
- Embeddings as input features
- Simple downstream models
- Combine with traditional variables
Why embeddings help prediction
- Capture complex patterns
- Reduce model complexity
- Improve generalisation
What embeddings change
- Less feature engineering
- Better transferability
- Faster iteration
What embeddings don’t change
- Remove ambiguity
- Eliminate bias in training data
- Replace domain knowledge
Common pitfalls
- Treating embeddings as objective
- Ignoring training context
- Over-interpreting clusters
When embeddings add most value
- Large spatial extent
- Few labels available
- Multiple tasks or regions
When traditional features may be better
- Small local studies
- Physically interpretable models
- Policy-driven analysis
Practical considerations
- Choice of pretrained model
- Spatial and temporal scale
- Validation strategy
About Lab II
- Unsupervised place typology
- Embeddings as core data
- Extension to prediction
Key takeaways
- Embeddings reshape workflows
- Enable reuse and scalability
- Require careful interpretation
Looking ahead
- Foundation models in EO
- Multi-modal embeddings
- Future geo-practice