A probabilistic geospatial modelling framework for forecasting urban sprawl dynamics from Earth observation data using Bayesian spatio-temporal machine learning.
Rapid urbanisation across Sub-Saharan Africa is frequently characterised by fragmented, low-density expansion that outpaces infrastructure provision and extends into peri-urban regions. While satellite-based Earth observation datasets have enabled detailed retrospective analyses of urban growth, relatively few approaches provide spatially explicit forecasts with quantified uncertainty suitable for municipal planning.
This project develops a probabilistic framework for forecasting urban sprawl processes. Using Yaoundé, Cameroon as a case study, historical urban expansion is reconstructed from multi-decadal Landsat imagery before forecasting future development patterns through 2035 using a Spatio-Temporal Sparse Variational Gaussian Process (ST-SVGP).
Unlike conventional deterministic forecasting approaches, this framework produces calibrated probabilistic predictions, enabling uncertainty-aware urban planning and supporting more informed land-use decision making.
- Reconstruct historical urban expansion (1990–2020) from Landsat imagery.
- Quantify urban sprawl using spatial indicators.
- Forecast future urban sprawl to 2035 using Bayesian spatio-temporal modelling.
- Quantify predictive uncertainty associated with future urban expansion.
- Develop a transferable framework for urban sprawl forecasting using Earth observation data.
Annual Landsat imagery is processed to reconstruct built-up land dynamics between 1990 and 2020.
Outputs include:
- Annual built-up extent
- Urban expansion maps
- Historical growth trajectories
Spatial analysis is used to derive quantitative indicators describing different forms of urban expansion, including:
- Leapfrog development
- Spatial dispersion
- Fragmentation
- Urban density characteristics
These metrics form the response variables used for forecasting future sprawl behaviour.
Future urban expansion is predicted using a
Spatio-Temporal Sparse Variational Gaussian Process (ST-SVGP)
which provides:
- Bayesian probabilistic predictions
- Predictive uncertainty estimates
- Scalability to long Earth observation time series
- Spatially explicit forecasts through 2035
Unlike deterministic neural networks or regression models, the ST-SVGP captures uncertainty associated with future land-use transitions, making predictions more suitable for evidence-based urban planning.
Landsat Time Series (1990–2020)
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Historical Built-up Mapping
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Urban Sprawl Metrics
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Feature Engineering
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ST-SVGP Model
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Probabilistic Urban Forecasts
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Uncertainty Analysis
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├── data/
│ ├── raw/
│ └── processed/
│
├── notebooks/
│
├── src/
│ ├── preprocessing/
│ ├── feature_engineering/
│ ├── models/
│ │ ├── st_svgp/
│ │ └── evaluation/
│ ├── analysis/
│ └── visualization/
│
├── outputs/
│ ├── forecasts/
│ ├── uncertainty/
│ ├── figures/
│ └── maps/
│
├── README.md
└── pyproject.toml
The primary boundary source is the Cameroon Common Operational Dataset for
Administrative Boundaries (cod-ab-cmr), provided by OCHA and originally
produced by the Institut National de Cartographie of Cameroon.
The seven ADM3 units corresponding to Yaoundé I–VII were selected using their administrative P-codes and dissolved to construct the Yaoundé administrative core. Their union was validated against the ADM2 Mfoundi boundary.
All processed geometries use EPSG:32632. A 5 km context buffer and a convex
hull are retained separately. The authoritative Landsat grid has a 30 m
resolution, a fixed (0, 0) anchor and stable global cell identifiers.
The project uses Landsat Collection 2 Tier 1 Level 2 Surface Reflectance imagery from:
- Landsat 5 TM:
LANDSAT/LT05/C02/T1_L2 - Landsat 7 ETM+:
LANDSAT/LE07/C02/T1_L2 - Landsat 8 OLI/TIRS:
LANDSAT/LC08/C02/T1_L2
The target observation epochs are:
1990, 1995, 2000, 2005, 2010, 2015 and 2020.
For each epoch, a three-year diagnostic period was queried to assess scene availability around the target year. Scene-level QA masking excludes fill, cloud, dilated cloud, cloud shadow, snow, cirrus where applicable and radiometric saturation. Water is retained as a valid observation.
Generated output:
- a complete Landsat scene manifest;
- scene-level valid coverage over the administrative core and context area;
- monthly availability summaries;
- candidate compositing-window comparisons;
- an epoch-level quality summary;
- the exact selected Earth Engine scene identifiers;
- a frozen compositing protocol and catalogue checksum.
The selected scene set is recorded in:
data/metadata/landsat/selected_scene_manifest.csvdata/metadata/landsat/compositing_protocol.yamldata/metadata/landsat/catalog_version.json
Final Landsat composites have not yet been generated. They will be constructed during ... by loading the exact frozen scene identifiers rather than re-querying the collections dynamically.
The following sources are planned for Day 3 and later stages but have not yet been integrated into the model-ready dataset:
- SRTM elevation and derived slope;
- OpenStreetMap roads and current infrastructure;
- GHSL built-up surface and population products.
GHSL will remain an auxiliary comparison and validation source, while current OpenStreetMap data will be documented carefully because its historical completeness varies.
Completed Landsat composites are converted into SAVI, MNDWI, NDBI, IBI, IBUI, VbSWIR1-BI and NDBSUI layers. Epoch-specific Otsu thresholds are estimated inside the Yaoundé administrative core and applied to the context grid.
The resulting binary maps are unvalidated candidate pseudo-labels. Missing or numerically undefined pixels remain masked and are not treated as non-built-up. Final index selection, comparative validation and temporal correction are performed in later stages.
Potential predictors include:
- Distance to roads
- Distance to urban centres
- Existing built-up areas
- Accessibility
- Topography
- Population density
- Additional environmental variables
Spatio-Temporal Sparse Variational Gaussian Process (ST-SVGP)
Advantages:
- Bayesian inference
- Scalable Gaussian Processes
- Probabilistic forecasting
- Spatial and temporal modelling
- Predictive uncertainty estimation
The framework generates:
- Historical urban expansion maps
- Urban sprawl indicator layers
- Probabilistic urban forecasts (2035)
- Predictive uncertainty maps
- Forecast evaluation metrics
Yaoundé, Cameroon
Yaoundé provides a representative example of a rapidly expanding Sub-Saharan African city experiencing dispersed urbanisation.
This framework is designed to support:
- Municipal land-use planning
- Urban growth management
- Infrastructure planning
- Sustainable urban development
- Earth observation research
- Spatio-temporal forecasting research
- Multi-decadal Earth observation analysis
- Bayesian spatio-temporal forecasting
- Spatially explicit uncertainty quantification
- Scalable Gaussian Process modelling
- Transferable workflow for rapidly urbanising regions
If you use this repository in academic work, please cite the associated publication once available.
Specify an appropriate open-source license (e.g., MIT, BSD-3-Clause, or GPL-3.0) before distribution.