The ISPRS International Contest on Individual Tree Crown Segmentation using High-Resolution Images and the Initial Findings

Abstract

Tree canopy plays an essential role in the biophysical activities in forest environment. During the past two decades, individual tree delineation using high-resolution imagery data has become a hot topic in forest sensing research. Individual Tree Crown (ITC) segmentation methods aim to generate masks that delineating the boundary of each ITC, which supports various tree parameter extractions. Thanks to the rapid development of deep learning, the ITC segmentation methods achieved remarkable improvement. However, existing research suffers from the limited availability of the datasets, and the lack of evaluation standards as well as task-orientated neural networks. In 2024, the International Society of Photogrammetry and Remote Sensing (ISPRS) launched the first International Contest of ITC Segmentation. The contest aims to reveal the state-of-the-art of the ITC method development using high-resolution images, to clarify the remaining barriers and challenges, and to guide further explorations in the field. This paper overviews the contest and reports the initial finding regarding the impact factors of the method performance.