Purpose of Review

Crown detection and measurement at the individual tree level provide detailed information for accurate forest management. To efficiently acquire such information, approaches to conduct individual tree detection and crown delineation (ITDCD) using remotely sensed data have been proposed. In recent years, deep learning, specifically convolutional neural networks (CNN), has shown potential in this field. This article provides a systematic review of the studies that used CNN for ITDCD and identifies major trends and research gaps across six perspectives: accuracy assessment methods, data types, platforms and resolutions, forest environments, CNN models, and training strategies and techniques.

Recent Findings

CNN models were mostly applied to high-resolution red–green–blue (RGB) images. When compared with other state-of-the-art approaches, CNN models showed significant improvements in accuracy. One study reported an increase in detection accuracy of over 11%, while two studies reported increases in F1-score of over 16%. However, model performance varied across different forest environments and data types. Several factors including data scarcity, model selection, and training approaches affected ITDCD results.

Summary

Future studies could (1) explore data fusion approaches to take advantage of the characteristics of different types of remote sensing data, (2) further improve data efficiency with customised sample approaches and synthetic samples, (3) explore the potential of smaller CNN models and compare their learning efficiency with commonly used models, and (4) evaluate impacts of pre-training and parameter tunings.

中文翻译：

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使用卷积神经网络 (CNN) 进行单棵树冠检测和描绘的系统综述

审查目的

单棵树级别的树冠检测和测量为准确的森林管理提供了详细信息。为了有效地获取此类信息，已经提出了使用遥感数据进行单棵树木检测和树冠描绘（ITDCD）的方法。近年来，深度学习，特别是卷积神经网络（CNN），在该领域显示出了潜力。本文对使用 CNN 进行 ITDCD 的研究进行了系统回顾，并从准确性评估方法、数据类型、平台和分辨率、森林环境、CNN 模型以及训练策略和技术六个角度确定了主要趋势和研究差距。

最近的发现

CNN 模型主要应用于高分辨率红绿蓝 (RGB) 图像。与其他最先进的方法相比，CNN 模型在准确性方面表现出显着提高。一项研究报告称检测准确度提高了 11% 以上，而两项研究报告称 F1 分数提高了 16% 以上。然而，不同森林环境和数据类型的模型性能各不相同。数据稀缺、模型选择和训练方法等多个因素影响了 ITDCD 结果。

概括

未来的研究可以（1）探索数据融合方法以利用不同类型遥感数据的特征，（2）通过定制样本方法和合成样本进一步提高数据效率，（3）探索较小CNN模型的潜力和将它们的学习效率与常用模型进行比较，并且（4）评估预训练和参数调整的影响。