Abstract
Automated semantic segmentation and object detection are of great importance in geospatial data analysis. However, supervised machine learning systems such as convolutional neural networks require large corpora of annotated training data. Especially in the geospatial domain, such datasets are quite scarce. Within this paper, we aim to alleviate this issue by introducing a new annotated 3D dataset that is unique in three ways: i) The dataset consists of both an Unmanned Aerial Vehicle (UAV) laser scanning point cloud and a 3D textured mesh. ii) The point cloud features a mean point density of about 800 pts/m2 and the oblique imagery used for 3D mesh texturing realizes a ground sampling distance of about 2–3 cm. This enables the identification of fine-grained structures and represents the state of the art in UAV-based mapping. iii) Both data modalities will be published for a total of three epochs allowing applications such as change detection. The dataset depicts the village of Hessigheim (Germany), henceforth referred to as H3D - either represented as 3D point cloud H3D(PC) or 3D mesh H3D(Mesh). It is designed to promote research in the field of 3D data analysis on one hand and to evaluate and rank existing and emerging approaches for semantic segmentation of both data modalities on the other hand. Ultimately, we hope that H3D will become a widely used benchmark dataset in company with the well-established ISPRS Vaihingen 3D Semantic Labeling Challenge benchmark (V3D). The dataset can be downloaded from https://ifpwww.ifp.uni-stuttgart.de/benchmark/hessigheim/default.aspx.
Highlights
Supervised Machine Learning (ML), especially embodied by Convolutional Neural Networks (CNNs), has become state of the art for automatic interpretation of various data
We aim to alleviate this issue by introducing a new annotated 3D dataset that is unique in three ways: i) The dataset consists of both an Unmanned Aerial Vehicle (UAV) laser scanning point cloud and a 3D textured mesh. ii) The point cloud features a mean point density of about 800 pts/m2 and the oblique imagery used for 3D mesh texturing realizes a ground sampling distance of about 2–3 cm
Semantic segmentation results submitted by participants of the benchmark are evaluated by the Hessigheim 3D (H3D) team by means of the derived confusion matrices
Summary
Supervised Machine Learning (ML), especially embodied by Convolutional Neural Networks (CNNs), has become state of the art for automatic interpretation of various data. Large datasets of labeled 2D imagery were established, for example the ImageNet dataset (Deng et al, 2009) As such an extensive annotation process cannot be accomplished by a single person or group, crowdsourcing was employed. First investigations were conducted on employing crowdworkers for 3D data annotation (Dai et al, 2017; Herfort et al, 2018; Walter et al, 2020; Ko€lle et al, 2020), these approaches typically try to avoid deriving a full pointwise annotation. This is achieved either by working on object level or by focusing only on necessary points by exploiting active learning techniques. In case of outdoor 3D data, existing datasets can be categorized into two different domains (comprehensive literature reviews are given by Griffiths and Boehm (2019) and Xie et al (2020)): terrestrial data and airborne data
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