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Embedding scale: new thinking of scale in machine learning and geographic representation

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Abstract

Concepts of scale are at the heart of diverse scientific endeavors that seek to understand processes and how observations and analyses influence our understanding. While disciplinary discretions exist, researchers commonly devise spatial, temporal, and organizational scales in scoping phenomena of interest and determining measurements and representational frameworks in research design. The rise of the Fourth Paradigm for science drives data-intensive computing without preconceived notions regarding at what scale the phenomena or processes of interest operate, or at which level of details meaningful patterns may emerge. While scale is the a priori consideration for theory-driven research to seek ontological and relational affirmations, big data analytics and machine learning embed scale in algorithms and model outputs. In this paper, we examine embedded scale in data-driven machine learning research, connect the embedding scale to scale operating in the general theory of geographic representation in GIS and scaffold our arguments with a study of using machine learning to detect archeological features in drone-collected high-density images.

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Data availability

Data and codes used in the case study are available at https://doi.org/10.6084/m9.figshare.19166348.

Notes

  1. Unsupervised machine learning clusters data into different groups based on distance measures and does not depend on labeled data. Recent developments in self-supervised machine learning attempts to reduce the reliance on massive, labeled data.

  2. A “feature” is a special term used by archeology to indicate any physical structure or element that is made or altered by humans, is not portable, cannot be removed from a site, and must be studied in context (ref. Glossary at Archaeological Institute of America—https://archaeological.org). To avoid conflating with the term “feature” in image processing and CNN feature maps, we use “archaeological features” for the physical structures noted on Kvamme’s map.

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Acknowledgements

We are indebted to the numerous individuals who have contributed to this paper. The PaleoCultural Research Group and the State Historical Society of North Dakota provided access to the Huff Indian Village State Historic Site and other sites surveyed for this project. Dr. Ken Kvamme graciously provided access and guidance for using his data from the site. We also thank the anonymous reviewers who’s comments through the review process made this paper stronger.

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  1. University of Texas at Dallas, 800 W. Campbell Road, GR31, Richardson, TX, 75080, USA

    May Yuan & Arlo McKee

  2. Texas Historical Commission, Austin, USA

    Arlo McKee

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  1. May Yuan
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  2. Arlo McKee
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Correspondence to May Yuan.

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Yuan, M., McKee, A. Embedding scale: new thinking of scale in machine learning and geographic representation. J Geogr Syst 24, 501–524 (2022). https://doi.org/10.1007/s10109-022-00378-6

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