CEUS paper

This week saw the first peer-reviewed publication related to the Urban Grammar see the light of day. Full coordinates of the paper are available here:

Singleton, A.; Arribas-Bel, D.; Murray, J.; Fleischmann, M. (2022). “Estimating generalized measures of local neighbourhood context from multispectral satellite images using a convolutional neural network”. Computers, Environment and Urban Systems, 95. 10.1016/j.compenvurbsys.2022.101802

@article{SINGLETON2022101802,
        title = {
            Estimating generalized measures of local neighbourhood context from
            multispectral satellite images using a convolutional neural network
        },
        journal = {Computers, Environment and Urban Systems},
        volume = {95},
        pages = {101802},
        year = {2022},
        issn = {0198-9715},
        doi = {https://doi.org/10.1016/j.compenvurbsys.2022.101802},
        url = {https://www.sciencedirect.com/science/article/pii/S0198971522000461},
        author = {Alex Singleton and Dani Arribas-Bel and John Murray and Martin Fleischmann},
        keywords = {
            Deep learning, Convolutional neural networks, Urban morphology,
            Multispectral satellite imagery, Cluster analysis
        },
        abstract = {
            The increased availability of high-resolution multispectral imagery captured by
            remote sensing platforms provides new opportunities for the characterisation and differentiation
            of urban context. The discovery of generalized latent representations from such data are however
            under researched within the social sciences. As such, this paper exploits advances in machine
            learning to implement a new method of capturing measures of urban context from multispectral
            satellite imagery at a very small area level through the application of a convolutional
            autoencoder (CAE). The utility of outputs from the CAE is enhanced through the application of spatial
            weighting, and the smoothed outputs are then summarised using cluster analysis to generate a typology
            comprising seven groups describing salient patterns of differentiated urban context. The limits of
            the technique are discussed with reference to the resolution of the satellite data utilised within
            the study and the interaction between the geography of the input data and the learned structure.
            The method is implemented within the context of Great Britain, however, is applicable to any
            location where similar high resolution multispectral imagery are available.
        }
}

This is technically not an Urban Grammar paper 100%, as it was a collab initiated and led by Alex Singleton and John Murray, both from the Geographic Data Science Lab. But it was a project we got involved in because of the Urban Grammar and its output is very much in line with the spirit of our project. A propos of the publication, Alex did a Twitter thread that summarises better than anything I could do the gist of the paper, why it’s important, and some of our thinking into how this feeds into broader efforts to measure and understand neighborhood context.

We are hugely excited about our results and the posibilities that using satellite technology, combined with a bit of machine learning and computer vision, opens up for building an understanding of cities at neighborhood level. The paper is open access, and the open data product we built around is, well… open; so, if you’re interested in this space (pun intended!) there is plenty for you to play with. If any of it stirs your interest further, do get in touch, we’d love to talk!

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