The Hessigheim 3D (H3D) Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Textured Meshes from UAV LiDAR and Multi-View-Stereo
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by
Michael Kölle, Dominik Laupheimer, Stefan Schmohl, Norbert Haala, Franz Rottensteiner, Jan Dirk Wegner, Hugo Ledoux
2021
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/sqm 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. 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.
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