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Outputs (262)

Refugee urbanism: seeing asylum ‘like a city’ (2020)
Journal Article
Darling, J. (2021). Refugee urbanism: seeing asylum ‘like a city’. Urban Geography, 42(7), 894-914. https://doi.org/10.1080/02723638.2020.1763611

This paper explores how asylum might be understood from an urban perspective. The paper focuses on a range of conceptual interventions mobilized around the notion of ‘seeing like a city’, which foreground the pragmatic and compositional nature of urb... Read More about Refugee urbanism: seeing asylum ‘like a city’.

Limits to scale invariance in alluvial rivers (2020)
Journal Article
Ferguson, R. (2021). Limits to scale invariance in alluvial rivers. Earth Surface Processes and Landforms, 46(1), 173-187. https://doi.org/10.1002/esp.5006

Assumptions about fluvial processes and process–form relations are made in general models and in many site‐specific applications. Many standard assumptions about reach‐scale flow resistance, bed‐material entrainment thresholds and transport rates, an... Read More about Limits to scale invariance in alluvial rivers.

Vulnerable Subjects (2020)
Book Chapter
Darling, J. (in press). Vulnerable Subjects. In H. Wilson, & J. Darling (Eds.), Research Ethics for Human Geography: A Handbook for Students (159-169). SAGE Publications

Expectations (2020)
Book Chapter
Darling, J. (in press). Expectations. In H. Wilson, & J. Darling (Eds.), Research Ethics for Human Geography: A Handbook for Students (73-81). SAGE Publications

Geography and Ethics (2020)
Book Chapter
Darling, J., & Wilson, H. (in press). Geography and Ethics. In H. Wilson, & J. Darling (Eds.), Research Ethics for Human Geography: A Handbook for Students (6-22). SAGE Publications

Spaces of Disaster (2020)
Book Chapter
Oven, K., Ruszczyk, H. A., & Rigg, J. (2020). Spaces of Disaster. In H. F. Wilson, & J. Darling (Eds.), Research Ethics for Human Geography (280-288). SAGE Publications

Adopting deep learning methods for airborne RGB fluvial scene classification (2020)
Journal Article
Carbonneau, P., Dugdale, S., Breckon, T., Dietrich, J., Fonstad, M., Miyamoto, H., & Woodget, A. (2020). Adopting deep learning methods for airborne RGB fluvial scene classification. Remote Sensing of Environment, 251, Article 112107. https://doi.org/10.1016/j.rse.2020.112107

Rivers are among the world's most threatened ecosystems. Enabled by the rapid development of drone technology, hyperspatial resolution (5 billion pixels were labelled and partitioned for the tasks of training (1 billion pixels) and validation (4 bill... Read More about Adopting deep learning methods for airborne RGB fluvial scene classification.