Integrating Crowd-sourced Annotations of Tree Crowns using Markov Random Field and Multispectral Information

Abstract

Benefiting from advancements in algorithms and computing capabilities, supervised deep learning models offer significant advantages in accurately mapping individual tree canopy cover, which is a fundamental component of forestry management. In contrast to traditional field measurement methods, deep learning models leveraging remote sensing data circumvent access limitations and are more cost-effective. However, the efficiency of models depends on the accuracy of the tree crown annotations, which are often obtained through manual labeling. The intricate features of the tree crown, characterized by irregular contours, overlapping foliage, and frequent shadowing, pose a challenge for annotators. Therefore, this study explores a novel approach that integrates the annotations of multiple annotators for the same region of interest. It further refines the labels by leveraging information extracted from multi-spectral aerial images. This approach aims to reduce annotation inaccuracies caused by personal preference and bias and obtain a more balanced integrated annotation.