Spatially explicit information on urban tree species distribution is crucial for green infrastructure management in cities. This information is usually acquired with ground-based surveys, which are time-consuming and usually cover limited spatial extents. The combination of machine learning algorithms and remote sensing images has been hailed as a promising way to map tree species over broad areas. Recently, convolutional neural networks (CNNs), a type of deep learning method, have achieved outstanding results for tree species discrimination in various remote sensing data types. However, there is a lack of studies using CNN-based methods to produce tree species composition maps, particularly for tropical urban settings. Here, we propose a multi-task CNN to map tree species in a highly diverse neighborhood in Rio de Janeiro, Brazil. Our network architecture takes an aerial photograph (RGB bands and pixel size = 0.15 m) and delivers two outputs: a semantically segmented image and a distance map transform. In the former, all pixel positions are labeled, while in the latter, each pixel position contains the Euclidean distance to the crown boundary. We developed a post-processing approach that combines the two outputs, and we classified nine and five tree species with an average F1-score of 79.3 ± 8.6% and 87.6 ± 4.4%, respectively. Moreover, our post-processing approach produced a realistic tree species composition map by labeling only pixels of the target species with high class membership probabilities. Our results show the potential of CNNs and aerial photographs to map tree species in highly diverse tropical urban settings, providing valuable insights for urban forest management and green spaces planning.

关于城市树种分布的空间明确信息对于城市的绿色基础设施管理至关重要。这些信息通常是通过基于地面的调查获得的，这些调查非常耗时并且通常覆盖有限的空间范围。机器学习算法和遥感图像的结合被誉为在广阔区域绘制树种的一种很有前途的方法。最近，卷积神经网络（CNN）作为一种深度学习方法，在各种遥感数据类型的树种判别方面取得了突出的成果。然而，缺乏使用基于 CNN 的方法来生成树种组成图的研究，特别是对于热带城市环境。在这里，我们提出了一个多任务 CNN 来绘制巴西里约热内卢高度多样化社区的树种。我们的网络架构采用航空照片（RGB 波段和像素大小 = 0.15 m）并提供两个输出：语义分割图像和距离图变换。在前者中，所有像素位置都被标记，而在后者中，每个像素位置包含到冠边界的欧几里德距离。我们开发了一种结合两个输出的后处理方法，我们对 9 种和 5 种树种进行了分类，平均 F1 分数分别为 79.3 ± 8.6% 和 87.6 ± 4.4%。此外，我们的后处理方法通过仅标记具有高级成员概率的目标物种的像素来生成逼真的树种组成图。我们的结果显示了 CNN 和航拍照片在高度多样化的热带城市环境中绘制树种的潜力，