Weakly-Supervised Learning for Tree Instances Segmentation in Airborne Lidar Point Clouds
This addresses forest monitoring by reducing labeling costs, but it is incremental as it builds on existing segmentation methods with a novel training feedback loop.
The paper tackles the problem of tree instance segmentation in airborne lidar point clouds, which is challenging due to data variations and expensive labeling, by proposing a weakly supervised approach using human quality ratings to train a rating model and fine-tune the segmentation model, resulting in a 34% improvement in correctly identified tree instances and reduced false positives.
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.