Comparative Assessment of GeoAI-Based Frameworks for Automatic Urban Tree Cover Mapping
TL;DR
This study evaluates and compares three Geospatial Artificial Intelligence frameworks for the automated detection and segmentation of tree cover and highlights the effectiveness of the TreeEyed QGIS plugin for urban applications and emphasizes the necessity of local LiDAR-derived data for model validation.
Abstract
Abstract. Accurate mapping of urban tree canopy is essential for quantifying ecosystem services and assessing the impact of green infrastructure on wellbeing and public health. This study evaluates and compares three Geospatial Artificial Intelligence (GeoAI) frameworks for the automated detection and segmentation of tree cover. The frameworks are YOLO, Detectree, and TreeEyed Utilizing high-resolution aerial imagery (0.2 m and 0.5 m ground sampling distance), the research tests different deep-learning paradigms, including object detection and semantic segmentation. The results indicate that while object-based models like YOLO align closely with statistical baselines (30.83% vs 30.11%), pixel-based models such as Detectree may underestimate fragmented urban vegetation. The study highlights the effectiveness of the TreeEyed QGIS plugin for urban applications and emphasizes the necessity of local LiDAR-derived data for model validation. Further studies would benefit from ad-hoc training with correct co-registration and consistent coordinate reference systems across layers.