128 research outputs found
The hybrid spatialities of transition: capitalism, legacy, and uneven urban economic restructuring
This paper conceptualises post-socialist urban economic geographies through the notion of hybrid spatialities that emerge from the mutual embeddedness of neoliberalism and socialist legacies. While the dismantling of state socialism was a massive moment towards the exacerbation of uneven development, ironically it is the socialist-era spatial legacy that has become the single major differentiating factor for the economic status of cities. This superficial overdetermination, however, masks the root causes of uneven development that must be seen in the logic of capitalism and its attendant practices which subsume legacy, recode its meaning, and recast the formerly equalitarian spaces as an uneven spatial order. The authors argue that the socialist legacy, rather than being an independent carrier of history, has been alienated from its history to become an infrastructure of neoliberalisation, conducive to capitalist process. The paper draws specifically on the experiences of Russia, although its reflections should reverberate much more broadly
Global wealth disparities drive adherence to COVID-safe pathways in head and neck cancer surgery
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Assessing biomass based on canopy height profiles using airborne laser scanning data in eucalypt plantations
(Re)developing old apartment blocks in Hanoi: government vision, local resistance and spatial routines
Automatic Delineation and Height Measurement of Regenerating Conifer Crowns under Leaf-Off Conditions Using UAV Imagery
The increasing use of unmanned aerial vehicles (UAV) and high spatial resolution imagery from associated sensors necessitates the continued advancement of efficient means of image processing to ensure these tools are utilized effectively. This is exemplified in the field of forest management, where the extraction of individual tree crown information stands to benefit operational budgets. We explored training a region-based convolutional neural network (Mask R-CNN) to automatically delineate individual tree crown (ITC) polygons in regenerating forests (14 years after harvest) using true colour red-green-blue (RGB) imagery with an average ground sampling distance (GSD) of 3 cm. We predicted ITC polygons to extract height information using canopy height models generated from digital aerial photogrammetric (DAP) point clouds. Our approach yielded an average precision of 0.98, an average recall of 0.85, and an average F1 score of 0.91 for the delineation of ITC. Remote height measurements were strongly correlated with field height measurements (rÂČ = 0.93, RMSE = 0.34 m). The mean difference between DAP-derived and field-collected height measurements was â0.37 m and â0.24 m for white spruce (Picea glauca) and lodgepole pine (Pinus contorta), respectively. Our results show that accurate ITC delineation in young, regenerating stands is possible with fine-spatial resolution RGB imagery and that predicted ITC can be used in combination with DAP to estimate tree height.Forestry, Faculty ofNon UBCForest Resources Management, Department ofReviewedFacult
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