DeepForest: A Python package for RGB deep learning tree crown delineation
DeepForest: A Python package for RGB deep learning tree crown delineation
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DOI：
10.1111/2041-210x.13472
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发表时间：
2020-07
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通讯作者：
Ben. G. Weinstein;S. Marconi;M. Aubry-Kientz;Grégoire Laurent Vincent;Henry Senyondo;E. White
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作者：
Ben. G. Weinstein;S. Marconi;M. Aubry-Kientz;Grégoire Laurent Vincent;Henry Senyondo;E. White
森林景观的遥感可以改变森林研究的速度、规模和成本。在森林分析中，遥感影像中单株树木的圈定是一项重要的任务。在这里，我们介绍了一个新的Python包DeepForest，它使用深度学习来检测高分辨率RGB图像中的单个树。虽然深度学习已被证明在一系列计算机视觉任务中非常有效，但它需要大量的训练数据，而这些数据通常难以在生态学研究中获得。DeepForest克服了这一限制，它包括一个模型，对来自22个森林的3000多万个算法生成的树冠进行预训练，并使用来自6个森林的10000个手工标记的树冠进行微调。该软件包支持将该通用模型应用于新数据，将模型微调到具有用户标记冠的新数据集，训练新模型以及评估模型预测。这简化了针对一系列森林、传感器和空间分辨率使用和再训练深度学习模型的过程。我们使用来自法属圭亚那热带森林国家生态观测站网络和俄勒冈州波特兰市街道树木的数据来说明DeepForest的工作流程。
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. 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 pre-trained on over 30 million algorithmically generated crowns from 22 forests and fine-tuned using 10,000 hand-labeled crowns from 6 forests. The package supports the application of this general model to new data, fine tuning the model to new datasets with user labeled 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. 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.