Urban Expansion and Building Footprint Extraction

30 years of city growth from Landsat, plus building-level detail from a modern vision transformer

Urban Expansion and Building Footprint Extraction
Advanced Geospatial AI 2 lessons Samuel Appiah Kubi

About this course

Two very different resolutions answer two very different questions about a growing African city: three decades of coarse Landsat imagery reveals the big picture of where a city has expanded, while a fine-tuned vision transformer extracts individual building footprints for planning at street level. This course builds both, using Accra, Kumasi, Lagos, Nairobi and Dar es Salaam — five of the fastest-growing urban areas on Earth — as running examples.

What you'll learn

  • Build a 30-year urban expansion map from Landsat time series and interpret its accuracy limits
  • Extract building footprints with a DINOv3 ViT-L/14 segmentation model
  • Apply LoRA fine-tuning with a modest number of local training polygons
  • Compute urban heat island intensity from Landsat thermal data
  • Generate a population-weighted exposure index for planning use

Requirements

  • Optical Analysis with Sentinel-2
  • Basic familiarity with deep learning concepts

Course content

30 Years of Landsat: Mapping Accra's Growth from 100 km² to 340 km² Preview 28 min
Building Footprint Extraction with DINOv3 32 min
Free
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  • LevelAdvanced
  • Lessons2
  • CertificateYes
  • AccessLifetime

Samuel Appiah Kubi Instructor