Deep Learning Semantic Segmentation Models for Detecting the Tree Crown Foliage

Abstract

Urban tree monitoring yields significant benefits to the environment and human society. Several aspects are essential to ensure the good condition of the trees and eventually predict their mortality or the risk of falling. So far, the most common strategy relies on the tree’s physical measures acquired from fieldwork analysis, which includes its height, diameter of the trunk, and metrics from the crown for a first glance condition analysis. The canopy of the tree is essential for predicting the resistance to extreme climatic conditions. However, the manual process is laborious considering the massive number of trees in the urban environment. Therefore, computer-aided methods are desirable to provide forestry managers with a rapid estimation of the tree foliage covering. This paper proposes a deep learning semantic segmentation strategy to detect the tree crown foliage in images acquired from the street-view perspective. The proposed approach employs several improvements to the well-known U-Net architecture in order to increase the prediction accuracy and reduce the network size. Compared to several vegetation indices found in the literature, the proposed model achieved competitive results considering the overlapping with the reference annotations.