Systematic maps of urban forests are useful for regional planners and ecologists to understand the spatial distribution of trees in cities. However, manually-created urban forest inventories are expensive and time-consuming to create and typically do not provide coverage of private land. Toward the goal of automating urban forest inventory through machine learning techniques, we performed a comparative study of methods for automatically detecting and localizing trees in multispectral aerial imagery of urban environments, and introduce a novel method based on convolutional neural network regression. Our evaluation is supported by a new dataset of over 1,500 images and almost 100,000 tree annotations, covering eight cities, six climate zones, and three image capture years. Our method outperforms previous methods, achieving 73.6% precision and 73.3% recall when trained and tested in Southern California, and 76.5% precision 72.0% recall when trained and tested across the entire state. To demonstrate the scalability of the technique, we produced the first map of trees across the entire urban forest of California. The map we produced provides important data for the planning and management of California’s urban forest, and establishes a proven methodology for potentially producing similar maps nationally and globally in the future.

中文翻译：

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使用高分辨率多光谱图像检测大规模城市环境中的单棵树


城市森林系统地图有助于区域规划者和生态学家了解城市树木的空间分布。然而，手动创建的城市森林清单成本高昂且耗时，并且通常不覆盖私人土地。为了通过机器学习技术实现城市森林清查自动化的目标，我们对城市环境多光谱航空图像中的树木自动检测和定位方法进行了比较研究，并引入了一种基于卷积神经网络回归的新方法。我们的评估得到了包含 1,500 多张图像和近 100,000 个树木注释的新数据集的支持，涵盖八个城市、六个气候区和三个图像捕获年份。我们的方法优于以前的方法，在南加州训练和测试时达到 73.6% 的精确度和 73.3% 的召回率，在整个州训练和测试时达到 76.5% 的精确度和 72.0% 的召回率。为了证明该技术的可扩展性，我们制作了第一张覆盖加州整个城市森林的树木地图。我们制作的地图为加州城市森林的规划和管理提供了重要数据，并为未来在全国和全球范围内制作类似地图建立了行之有效的方法。