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

• DOI: 10.1016/j.jag.2022.102764
• Publication Date: 2022-04-04T16:23:27Z
• Authors: Mingxia Yang; Yuling Mou; Shan Liu; Yanrong Meng; Zelin Liu; Peng Li; Wenhua Xiang; Xiaolu Zhou; Changhui Peng
• Keywords: Tree (set theory)

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