Detecting and mapping tree crowns based on convolutional neural network and Google Earth images

Abstract

Mapping tree crown is critical for estimating the functional and spatial distribution of ecosystem services. However, accurate and up-to-date urban crown mapping remains a challenge due to the time-consuming nature of field sampling and spatial heterogeneity. Another challenge is the data cost, which is always a concern for low-cost processing of forest maps on large scales. Here, we developed a novel working framework by integrating an advanced deep learning technology, the Mask Region-based Convolutional Neural Network (Mask R-CNN) model with Google Earth images to detect tree crown cover in New York’s Central Park, which is a typical testbed for an urban forest area with highly heterogeneous tree crown cover. The results indicated that the tree number detection rate estimated by the Mask R-CNN crown detection model was 82.8% and the crown area detection rate was 81.8% for the entire study area. The model detected isolated trees and closed forest trees areas with a recall of 87.5% and 81.6% of the tree numbers, respectively. The analysis indicates that the tree crown detection model could accurately detect tree crowns under highly complex environments and demonstrates great potential to map urban tree crown covers.