A Shape Prior-Driven Multiscale Individual Tree Crown Delineation Method Using Oblique Photogrammetry in Urban Areas

Individual tree crown delineation (ITCD) in urban areas is essential for various downstream applications, such as refined urban modeling, urban forest inventory, and individual tree management. Thanks to the soaring development of the uncrewed aerial vehicle (UAV)-based photogrammetry technique, swift 3-D data collection is plausible at the city scale. However, a fully automatic and precise ITCD method is desired for the photogrammetry point cloud data. Previous ITCD approaches have faced significant scale inadaptability and low robustness problems, especially for the scenario in this article. We introduce a novel normal direction accumulation tree center detection (NDACD) method to fully leverage the rich side information from the oblique photogrammetry data, extending beyond just the top-of-canopy perspective. A multiscale segmentation workflow is proposed to automatically adapt to the diversity of crown scales across different tree species. Additionally, the multiscale segmentation workflow is guided by high-level shape priors (SPs), such as convexity and tightness, to further enhance the robustness to complex tree crown structures. We tested our method on a dataset of more than 700 trees collected by oblique photogrammetry in Shenzhen and Wuhan, China, demonstrating its effectiveness and robustness with low-quality real-world data. Compared to other methods, our approach achieves a higher ITCD accuracy, with an Acc of 91.2% and an average intersection over union (IoU) of 86.9%. Furthermore, it shows good adaptability to alternative data sources, including drone and mobile light detection and ranging (LiDAR) data.