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AI powered tree mapping in urban, rural and forest environments
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Mapping individual trees is useful for a wide range of applications including forest management, urban planning, carbon monitoring and ecological modelling. Previously, various remote sensing techniques were employed in New Zealand to detect individual trees from 3D LiDAR point clouds and aerial imagery over large areas. A reliable approach for region-wide studies involved a canopy height-based method, which first identifies treetops using local maxima and then segments crowns using region-growing algorithms. However, recent advancements in artificial intelligence (AI) and high-performance computing enable more flexible and accurate tree mapping on a larger scale. In particular, Mask R-CNN, a region-based Convolutional Neural Network, is used globally to detect tree crowns from remote sensing imagery.
Jan Schindler leads a data science programme that applies these methods in diverse environments in New Zealand, including urban, silvopastoral and native forests using multi-resolution remote sensing data. In this presentation we showcase three key studies (i) detecting 1.8 million urban and forest trees in Wellington City, (ii) mapping different tree species on pastoral hill-country farms to inform landslide susceptibility modelling and (iii) delineating individual tree crowns in a complex native forest plot using high-resolution drone imagery. We will discuss the strengths and practical limitations of these novel methods in the New Zealand context.
Jan Schindler leads a data science programme that applies these methods in diverse environments in New Zealand, including urban, silvopastoral and native forests using multi-resolution remote sensing data. In this presentation we showcase three key studies (i) detecting 1.8 million urban and forest trees in Wellington City, (ii) mapping different tree species on pastoral hill-country farms to inform landslide susceptibility modelling and (iii) delineating individual tree crowns in a complex native forest plot using high-resolution drone imagery. We will discuss the strengths and practical limitations of these novel methods in the New Zealand context.
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