Abstract
Tree instance segmentation of airborne laser scanning (ALS) data is of utmost importance for forest monitoring, but remains challenging due to variations in the data caused by factors such as sensor resolution, vegetation state at acquisition time, terrain characteristics, etc. Moreover, obtaining a sufficient amount of precisely labeled data to train fully supervised instance segmentation methods is expensive. To address these challenges, we propose a weakly supervised approach where labels of an initial segmentation result obtained either by a non-finetuned model or a closed form algorithm are provided as a quality rating by a human operator. The labels produced during the quality assessment are then used to train a rating model, whose task is to classify a segmentation output into the same classes as specified by the human operator. Finally, the segmentation model is finetuned using feedback from the rating model. This in turn improves the original segmentation model by 34\% in terms of correctly identified tree instances while considerably reducing the number of non-tree instances predicted. Challenges still remain in data over sparsely forested regions characterized by small trees (less than two meters in height) or within complex surroundings containing shrubs, boulders, etc. which can be confused as trees where the performance of the proposed method is reduced.
Abstract (translated)
基于航空激光扫描（ALS）数据的树实例分割对于森林监测至关重要，但由于传感器分辨率、采集时植被状态、地形特征等因素的影响，这一任务仍然具有挑战性。此外，获取足够数量的精确标注数据以训练完全监督的实例分割方法是非常昂贵的。为了解决这些难题，我们提出了一种弱监督的方法，在这种方法中，初始分割结果（通过未经微调的模型或封闭形式算法获得）由人工操作员提供质量评分。在质量评估过程中产生的标签被用来训练一个评级模型，其任务是将分割输出分类为人工作业所指定的相同类别。最后，使用评级模型提供的反馈对分割模型进行微调，这反过来又使原始分割模型正确识别树实例的数量提高了34%，同时显著减少了误判为非树实例的数量。 然而，在稀疏森林区域（树木高度小于两米）或复杂环境中（如灌木、巨石等），数据仍然存在挑战。在这些情况下，由于与周围环境的混淆问题，所提出的方法性能会有所下降。
URL
https://arxiv.org/abs/2508.15646
