Using deep learning to identify (urban) form and function in satellite imagery
The case of Great Britain

Dani Arribas-Bel
Martin Fleischmann
[@darribas]
[@martinfleis]

Spatial Signatures

A characterisation of space based on form and function designed to understand urban environments

A characterisation of space based on form and function designed to understand urban environments

A characterisation of space based on form and function designed to understand urban environments

A characterisation of space based on form and function designed to understand urban environments

BRITISH SIGNATURES

Countryside (3)

Periphery (4)

Urban (9)

urbangrammarai.xyz/story/

 🛰

Sentinel 2

What do we want to do?

train a neural network
understand the role of geography

Chip size effect

80x80m: 13760 chips, which is 74 % of maximum

160x160m: 2718 chips within, which is 57 % of maximum

320x320m: 423 chips within, which is 35 % of maximum

640x640m: 38 chips within, which is 13 % of maximum

320x320m, chips capturing the proportion (100% of maximum)

Sliding

Probability modelling

Preview of results

Image classification - Overall accuracy 42.8%

Multi-output regression - Overall accuracy 43.5%

Image classification - Wild countryside

Multi-output regression - Wild countryside

Image classification - Urbanity

Multi-output regression - Urbanity

Multi-output regression - Predicted class (320x320m)

The takeaway

  • Better (spatial) evaluation of model performance
  • Probability modeling: does it make (any) sense?
  • Anything else?

Using deep learning to identify (urban) form and function in satellite imagery
The case of Great Britain

Dani Arribas-Bel
Martin Fleischmann
[@darribas]
[@martinfleis]