Remembering Both the Machine and the Crowd When Sampling Points: Active Learning for Semantic Segmentation of ALS Point Clouds

Abstract

Supervised Machine Learning systems such as Convolutional Neural Networks (CNNs) are known for their great need for labeled data. However, in case of geospatial data and especially in terms of Airborne Laserscanning (ALS) point clouds, labeled data is rather scarce, hindering the application of such systems. Therefore, we rely on Active Learning (AL) for significantly reducing necessary labels and we aim at gaining a deeper understanding on its working principle for ALS point clouds. Since the key element of AL is sampling of most informative points, we compare different basic sampling strategies and try to further improve them for geospatial data. While AL reduces total labeling effort, the basic issue of experts doing this labor- and therefore cost-intensive task remains. Therefore, we propose to outsource data annotation to the crowd. However, when employing crowdworkers, labeling errors are inevitable. As a remedy, we aim on selecting points, which are easier for interpretation and evaluate the robustness of AL to labeling errors. Applying these strategies for different classifiers, we estimate realistic segmentation results from crowdsourced data solely, only differing in Overall Accuracy by about 3% points compared to results based on completely labeled dataset, which is demonstrated for two different scenes.