1. Remote sensing of forested landscapes can transform the speed, scale and cost of forest research. The delineation of individual trees in remote sensing images is an essential task in forest analysis. Here we introduce a new Python package, DeepForest that detects individual trees in high resolution RGB imagery using deep learning.
2. While deep learning has proven highly effective in a range of computer vision tasks, it requires large amounts of training data that are typically difficult to obtain in ecological studies. DeepForest overcomes this limitation by including a model pretrained on over 30 million algorithmically generated crowns from 22 forests and fine‐tuned using 10,000 hand‐labelled crowns from six forests.
3. The package supports the application of this general model to new data, fine tuning the model to new datasets with user labelled crowns, training new models and evaluating model predictions. This simplifies the process of using and retraining deep learning models for a range of forests, sensors and spatial resolutions.
4. We illustrate the workflow of DeepForest using data from the National Ecological Observatory Network, a tropical forest in French Guiana, and street trees from Portland, Oregon.

中文翻译：

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DeepForest：用于RGB深度学习树冠描绘的Python包

1. 遥感森林景观可以改变森林研究的速度，规模和成本。遥感图像中单个树木的描绘是森林分析中的一项基本任务。在这里，我们介绍了一个新的Python包DeepForest，该包使用深度学习功能来检测高分辨率RGB图像中的单个树。
2. 虽然深度学习已证明在一系列计算机视觉任务中非常有效，但它需要大量的培训数据，而这些数据通常在生态研究中很难获得。DeepForest克服了这一限制，它包括一个模型，该模型在来自22个森林的3000万个算法生成的树冠上进行了预训练，并使用来自六个森林的10,000个手工标记的树冠进行了微调。
3. 该软件包支持将该通用模型应用到新数据，使用用户标记的冠冕将模型微调为新数据集，训练新模型并评估模型预测。这简化了针对一系列森林，传感器和空间分辨率使用和重新训练深度学习模型的过程。
4. 我们使用来自国家生态观测站网络，法属圭亚那的热带森林和俄勒冈州波特兰市的行道树的数据说明DeepForest的工作流程。