Conference paper Open Access
Using deep learning to identify (urban) form and function in satellite imagery - the case of Great Britain
Fleischmann, Martin; Arribas-Bel, Daniel
In this paper, we introduce a framework to leverage satellite data through AI to build rich representations of urban form and function. We use a concept of Spatial signatures to characterise predominantly urban environment into data-driven classes based on both form and function composed of a large number of input data sources. Consequently, we explore the ability of Sentinel 2 satellite imagery and state-of-the-art AI models to capture the same classification of space using a single, regularly updated data source, including the conceptual questions of relationship between granular signature geometry and rigid raster grid of satellite data.
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GISRUK_2022_paper_72.pdf
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- Publication date:
- April 3, 2022
- DOI:
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- Meeting:
- 30th Annual Geographical Information Science Research UK (GISRUK), Liverpool, United Kingdom, 5th – 8th April 2022
- Communities:
- License (for files):
- Creative Commons Attribution 4.0 International