Morphological Embedding Model for Urban Experience
Computes ~120 morphological features per H3 cell from OpenStreetMap, then embeds each cell into a 64-dimensional vector space. Applied across 6 cities on 4 continents.
Pipeline
Four stages transform raw map geometry into a queryable embedding corpus.
- Feature Engineering — ~120 morphological metrics per H3 cell from OSM — road density, building coverage, block shape, perimeter ratios, and more.
- Normalisation — Per-city standardisation ensures cross-continental comparability despite different urban scales.
- Autoencoding — A lightweight autoencoder compresses the feature matrix into a 64-dimensional vector per cell.
- Vector Search — Nearest-neighbour search in embedding space reveals morphological twins across cities in milliseconds.
Embeddings & Urban Form
MORPHEME methodology paper in preparation. Application to slavery and war geographies ongoing.
