Urban forests play a fundamental and irreplaceable role within cities through the ecosystem services they provide, such as carbon capture. However, inadequate management of urban trees can heighten the risks they pose to society. For instance, mechanical failures of tree components, such as branches, can cause harm to individuals and property. Regular assessments of tree conditions are necessary to mitigate these tree-related hazards, yet such evaluations are labor-intensive and currently lack automation. Previous studies have proposed utilizing street view images to alleviate tree inspection and shown the feasibility of visually inspecting trees. However, only a limited number of studies have addressed the automatic evaluation of urban trees, a challenge that can potentially be addressed using deep learning networks. Particularly in urban environments, there is a pressing need for increased automation in unresolved computer vision tasks. Therefore, this research presents a comprehensive analysis of neural networks and publicly available datasets that can aid arborists in automatically identifying urban trees. Specifically, we investigate the potential of deep learning networks in classifying tree genera and segmenting individual trees and their trunks. We emphasize the utilization of transfer learning strategies to enhance tree identification. The results demonstrate that neural networks can be considered practical tools for assisting arborists in tree recognition. Nevertheless, there are still gaps that remain and require attention in future research endeavors.

中文翻译：

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计算机视觉作为通过街景图像筛选城市树木的工具面临的挑战

城市森林通过其提供的生态系统服务（例如碳捕获）在城市中发挥着基本且不可替代的作用。然而，城市树木管理不善可能会增加它们对社会造成的风险。例如，树枝等树木部件的机械故障可能会对个人和财产造成伤害。定期评估树木状况对于减轻这些与树木相关的危害是必要的，但此类评估是劳动密集型的，并且目前缺乏自动化。先前的研究提出利用街景图像来减轻树木检查，并展示了目视检查树木的可行性。然而，只有有限数量的研究解决了城市树木的自动评估，这是一个可以使用深度学习网络解决的挑战。特别是在城市环境中，迫切需要提高未解决的计算机视觉任务的自动化程度。因此，这项研究对神经网络和公开数据集进行了全面分析，可以帮助树木学家自动识别城市树木。具体来说，我们研究了深度学习网络在树属分类和个体树及其树干分割方面的潜力。我们强调利用迁移学习策略来增强树木识别。结果表明，神经网络可以被认为是辅助树木学家进行树木识别的实用工具。尽管如此，仍然存在差距，需要在未来的研究工作中予以关注。