Abstract
Abstract. Deep learning models achieve excellent semantic segmentation results for airborne laser scanning (ALS) point clouds, if sufficient training data are provided. Increasing amounts of annotated data are becoming publicly available thanks to contributors from all over the world. However, models trained on a specific dataset typically exhibit poor performance on other datasets. I.e., there are significant domain shifts, as data captured in different environments or by distinct sensors have different distributions. In this work, we study this domain shift and potential strategies to mitigate it, using two popular ALS datasets: the ISPRS Vaihingen benchmark from Germany and the LASDU benchmark from China. We compare different training strategies for cross-city ALS point cloud semantic segmentation. In our experiments, we analyse three factors that may lead to domain shift and affect the learning: point cloud density, LiDAR intensity, and the role of data augmentation. Moreover, we evaluate a well-known standard method of domain adaptation, deep CORAL (Sun and Saenko, 2016). In our experiments, adapting the point cloud density and appropriate data augmentation both help to reduce the domain gap and improve segmentation accuracy. On the contrary, intensity features can bring an improvement within a dataset, but deteriorate the generalisation across datasets. Deep CORAL does not further improve the accuracy over the simple adaptation of density and data augmentation, although it can mitigate the impact of improperly chosen point density, intensity features, and further dataset biases like lack of diversity.
Highlights
Unordered point clouds in 3D space have become a standard representation of spatial data, used across a wide range of applications like digital mapping, building information modelling and transportation planning
In the original ISPRS Vaihingen and LASDU datasets, the LiDAR return intensities have already been scaled to [0, 255], so we directly concatenate them with the 3D point coordinates and feed the resulting 4D points to the network
We have empirically investigated cross-city learning of semantic segmentation for airborne laser scanning (ALS) point clouds, using example datasets from Germany and China
Summary
Unordered point clouds in 3D space have become a standard representation of spatial data, used across a wide range of applications like digital mapping, building information modelling and transportation planning. An important task for many such applications is semantic segmentation, i.e., assigning a semantic class label to every point. Deep learning relies on large quantities of annotated reference data. E.g., labelling 2km of ALS data from Dublin (Ireland) into 13 hierarchical multi-level classes took >2,500 person-hours (Zolanvari et al, 2019). More and more annotated ALS data is available in public datasets and benchmarks, labelled according to various nomenclatures. If models trained from such public data (source scenes) could be transferred to other target scenes, per-project annotation would become obsolete. In practice almost every project (including the public datasets) is different in terms of source and target environment. In particular deep learning models, will tend to overfit to the source data and deliver poor results when naively applied to new, previously unseen target data
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