<p><strong>Abstract.</strong> Semantic segmentation of point clouds is one of the main steps in automated processing of data from Airborne Laser Scanning (ALS). Established methods usually require expensive calculation of handcrafted, point-wise features. In contrast, Convolutional Neural Networks (CNNs) have been established as powerful classifiers, which at the same time also learn a set of features by themselves. However, their application to ALS data is not trivial. Pure 3D CNNs require a lot of memory and computing time, therefore most related approaches project ALS point clouds into two-dimensional images. Sparse Submanifold Convolutional Networks (SSCNs) address this issue by exploiting the sparsity often inherent in 3D data. In this work, we propose the application of SSCNs for efficient semantic segmentation of voxelized ALS point clouds in an end-to-end encoder-decoder architecture. We evaluate this method on the ISPRS Vaihingen 3D Semantic Labeling benchmark and achieve state-of-the-art 85.0% overall accuracy. Furthermore, we demonstrate its capabilities regarding large-scale ALS data by classifying a 2.5&thinsp;km<sup>2</sup> subset containing 41&thinsp;M points from the Actueel Hoogtebestand Nederland (AHN3) with 95% overall accuracy in just 48&thinsp;s inference time or with 96% in 108&thinsp;s.</p>
Abstract. Automated semantic interpretation of 3D point clouds is crucial for many tasks in the domain of geospatial data analysis. For this purpose, labeled training data is required, which has often to be provided manually by experts. One approach to minimize effort in terms of costs of human interaction is Active Learning (AL). The aim is to process only the subset of an unlabeled dataset that is particularly helpful with respect to class separation. Here a machine identifies informative instances which are then labeled by humans, thereby increasing the performance of the machine. In order to completely avoid involvement of an expert, this time-consuming annotation can be resolved via crowdsourcing. Therefore, we propose an approach combining AL with paid crowdsourcing. Although incorporating human interaction, our method can run fully automatically, so that only an unlabeled dataset and a fixed financial budget for the payment of the crowdworkers need to be provided. We conduct multiple iteration steps of the AL process on the ISPRS Vaihingen 3D Semantic Labeling benchmark dataset (V3D) and especially evaluate the performance of the crowd when labeling 3D points. We prove our concept by using labels derived from our crowd-based AL method for classifying the test dataset. The analysis outlines that by labeling only 0:4% of the training dataset by the crowd and spending less than 145 $, both our trained Random Forest and sparse 3D CNN classifier differ in Overall Accuracy by less than 3 percentage points compared to the same classifiers trained on the complete V3D training set.
ABSTRACT:Photogrammetric data capture of complex 3D objects using UAV imagery has become commonplace. Software tools based on algorithms like Structure-from-Motion and multi-view stereo image matching enable the fully automatic generation of densely meshed 3D point clouds. In contrast, the planning of a suitable image network usually requires considerable effort of a human expert, since this step directly influences the precision and completeness of the resulting point cloud. Planning of suitable camera stations can be rather complex, in particular for objects like buildings, bridges and monuments, which frequently feature strong depth variations to be acquired by high resolution images at a short distance. Within the paper, we present an automatic flight mission planning tool, which generates flight lines while aiming at camera configurations, which maintain a roughly constant object distance, provide sufficient image overlap and avoid unnecessary stations. Planning is based on a coarse Digital Surface Model and an approximate building outline. As a proof of concept, we use the tool within our research project MoVEQuaD, which aims at the reconstruction of building geometry at sub-centimetre accuracy.
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