Urban Tree Canopy Mapping and Analysis Using Iterative Annotation Method and Deep Learning: A Case Study in Beijing

Urban trees have significant ecological and social functions, and generating Urban Tree Canopy (UTC) maps is an effective method for understanding their distribution. However, existing studies primarily rely on medium- to low-resolution imagery for large-scale extraction or high-resolution imagery for small-scale extraction, making it challenging to balance spatial coverage and accuracy. To comprehensively analyze the tree canopy characteristics of urban trees across large areas, this study selects Beijing as the research area, and employs highresolution remote sensing imagery with deep learning techniques to construct the UTC map of Beijing. Deep learning models typically require large amounts of labeled data for training, but manually annotating pixel-level labels is time-consuming and costly. To address this, we propose an iterative method based on Intersection over Union (IoU), integrating multi-scale segmentation and nearest-neighbor classification algorithms. By leveraging a limited number of labeled pixels, this approach efficiently generates a extensive labeled data, enabling the rapid construction of a high-precision dataset. Using this method, we established the Beijing Urban Tree Canopy Dataset (BUTCD). By Employing BUTCD and InternImage T+UPerNet as the tree canopy segmentation network, we generated the urban tree canopy map for Beijing. Accuracy assessments demonstrated an accuracy of 0.9806, precision of 0.9569, recall of 0.9702, mean IoU (mIoU) of 0.9296, and F1-score of 0.9635. We analyzed the results of Beijing's urban tree canopy, revealing that the tree canopy coverage rate is approximately 43.99%, with higher coverage in the northwest and lower coverage in the southeast. The per capita tree canopy area in Beijing was calculated as 330.29 m². Within Beijing's central urban area, trees outside forests account for 26.54% of the the total urban tree population, indicating that conventional statistics on tree-related data in this area tend to underestimate the true value by about onefourth. This underestimation may originate from the substantial contributions of trees outside forest areas being neglected during the statistical process, especially in the central urbanized districts. We anticipate that the urban tree canopy map of Beijing will facilitate research on the city's ecosystem services, advance sustainable urban development, and improve the quality of life for residents.