How to Place Vertiport Locations in Any City Using Geospatial Machine Learning

A method is presented for identifying optimal vertiport locations in cities with incomplete data, using Lagos as a case study. The initial approach uses K-means clustering on population data alone, which results in unsuitable sites on water or near airports. To improve this, the model is refined by adding accessibility scores for roads and transit, as well as exclusion zones for unbuildable or restricted areas. This multi-layered geospatial machine learning pipeline produces a more realistic and actionable shortlist of potential locations that can be reproduced for other cities.