Reproducible analysis of terrain slope and Google Open Buildings detection recall in highland Ethiopia.
# Terrain slope and open building-footprint completeness
Reproducibility materials for a study comparing Google Open Buildings v3 with
hand-labelled ORBITaL-Net reference buildings in highland Ethiopia.
The analysis asks whether building-detection recall varies with terrain slope
after controlling for tree cover and reference-imagery acquisition year. The
study measures an association; it does not identify a single optical or
machine-learning mechanism.
## Reproduced headline result
The checked-in processed tables reproduce the paper's primary sample and model:
- 1,269 chips with at least one reference building and verified detection coverage
- 16,336 reference buildings after the pre-specified 10-500 m2 area filter
- 12,770 matched detections
- sample recall: 78.17%
- terrain-slope effect: approximately -7.5 percentage points per +10 degrees
in the pooled model
- terrain-slope effect: approximately -5.7 percentage points per +10 degrees
with source-scene fixed effects
These are model-implied discrete probability changes evaluated at the sample
mean recall, not average marginal effects. Confidence intervals use
source-scene clustered sandwich covariance.
## Quick reproduction
```bash
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python src/reproduce_results.py
python src/verify_reproduction.py
```
Generated tables and figures are written to `results/`.
The same reproduction check runs automatically on GitHub Actions after every
push. Dependencies for the recovered geospatial-preparation scripts are listed
separately in `requirements-geospatial.txt`.
## Repository layout
```text
data/processed/ Small derived tables needed to reproduce the models
docs/DATA_SOURCES.md Upstream datasets, licenses, and acknowledgements
src/reproduce_results.py Exact grouped-binomial GLMs and figures
src/verify_reproduction.py Regression checks against the manuscript values
src/recovered_pipeline/ Original data-preparation scripts …