Customized Atrous Spatial Pyramid Pooling with Joint Convolutions for Urban Tree Segmentation

Abstract

Urban trees provide several benefits to the cities, including local climatic regulation and better life quality. Assessing the tree conditions is essential to gather important insights related to its biomechanics and the possible risk of falling. The common strategy is ruled by fieldwork campaigns to collect the tree’s physical measures like height, the trunk’s diameter, and canopy metrics for a first-glance assessment and further prediction of the possible risk to the city’s infrastructure. The canopy and trunk of the tree play an important role in the resistance analysis when exposed to severe windstorm events. However, fieldwork analysis is laborious and time-expensive because of the massive number of trees. Therefore, strategies based on computational analysis are highly demanded to promote a rapid assessment of tree conditions. This paper presents a deep learning-based approach for semantic segmentation of the trunk and canopy of trees in images acquired from the street-view perspective. The proposed strategy combines convolutional modules, spatial pyramid pooling, and attention mechanism into a U-Net-based architecture to improve the prediction capacity. Experiments performed over two image datasets showed the proposed model attained competitive results compared to previous works employing large-sized semantic segmentation models.