Journal Article

Comparison of manual and semi-automated synthetic training data creation for individual tree crown delineation

Steier, Janik, Iwaszczuk, Dorota

2025

Overview

Deep learning models in the field of individual tree detection and crown delineation (ITDCD) rely on large and high-quality annotation datasets to produce accurate predictions. Training data or annotations for most ITDCD studies are collected through manual labeling. Manual labeling, especially for complex structures like tree crowns, is a time-consuming process that often results in error-prone annotations. Error-prone annotations, in turn, can lead to significant errors in the predictions of deep learning models. Semi- or fully-automated training data creation shows the potential to make the creation process more efficient and ensure high quality of the training dataset. In this work, we present a methodology for generating semi-automated synthetic training data for deep learning-based ITDCD applications. Furthermore, a systematic criteria-based - validity, efficiency, variety and scalability - comparison is conducted between the manual and synthetic training data creation methods to structurally and practically illustrate the advantages and disadvantages of the two approaches. Overall, the semi-automated synthetic data approach outperforms manual labeling in terms of validity, efficiency, and scalability; once the algorithm is implemented, it rapidly generates arbitrarily large, high-quality, reproducible tree crown annotation datasets. In contrast, a manual creation approach shows its advantages as an efficient way to create small, low-quality datasets (e.g., for fine-tuning a pre-trained model) compared to developing a semi-automated method from scratch.

• Publisher: Copernicus GmbH,Copernicus Publications
• Subject: Annotations / Automation / Datasets / Deep learning / Delineation / Labeling / Machine learning / Synthetic data / Training / Trees