The most established and widely used methods for analysing tree images for tasks such as geometry analysis, segmentation and classification often rely on pixels. In this paper, the applicability of analyzing tree geometry based on a graph representation rather than a pixel-based
...

The most established and widely used methods for analysing tree images for tasks such as geometry analysis, segmentation and classification often rely on pixels. In this paper, the applicability of analyzing tree geometry based on a graph representation rather than a pixel-based approach is pursued. To do so, 2D renders of different species of trees are converted to spatial graph structures capturing significant points on the tree skeleton. Two independent Graph Convolutional Network algorithms which learn node (coordinate) features are then applied on the obtained dataset to assess the reliability of graph based analysis. The first experiment explores a GCN for assigning correct species labels to the skeleton graph of the original tree image, demonstrating the association between geometry and tree metadata. The second experiment, an unsupervised representation learning, is conducted by using Graph Autoencoders to obtain an embedding for each skeleton graph which can be used to reconstruct partially the same graph, demonstrating the association between GCE latent representation and geometry. Promising results were found in both cases, reinforcing the reliability of the original proposition to rely on geometry as well as pixels for tree analysis tasks.