We introduce a novel deep learning method for detection of individual trees in urban environments using high-resolution multispectral aerial imagery. We use a convolutional neural network to regress a confidence map indicating the locations of individual trees, which are localized using a peak finding algorithm. Our method provides complete spatial coverage by detecting trees in both public and private spaces, and can scale to very large areas. We performed a thorough evaluation of our method, 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. We trained our model on data from Southern California, and achieved a precision of 73.6% and recall of 73.3% using test data from this region. We generally observed similar precision and slightly lower recall when extrapolating to other California climate zones and image capture dates. We used our method to produce a map of trees in the entire urban forest of California, and estimated the total number of urban trees in California to be about 43.5 million. Our study indicates the potential for deep learning methods to support future urban forestry studies at unprecedented scales.

翻译：本文提出了一种利用高分辨率多光谱航空影像进行城市环境单木检测的新型深度学习方法。我们采用卷积神经网络回归生成表征单木位置的置信度图，并通过峰值检测算法实现树木定位。该方法通过同时检测公共与私有空间中的树木实现完整的空间覆盖，并具备处理超大规模区域的能力。我们基于覆盖八个城市、六个气候带和三个影像获取年份的新建数据集（包含1,500余幅影像及近10万棵树木标注）对本方法进行了全面评估。使用南加州数据训练模型后，在该区域测试数据上取得了73.6%的精确率与73.3%的召回率。在推广至加州其他气候带与不同影像获取时间时，模型普遍保持相近的精确率与略低的召回率。应用本方法生成了加州全域城市森林树木分布图，估算加州城市树木总数约为4,350万棵。本研究表明深度学习方法具备支撑未来超大规模城市林业研究的潜力。