Nationwide Point Cloud—The Future Topographic Core Data

Virtanen, Juho‐Pekka; Kukko, Antero; Kaartinen, Harri; Jaakkola, Anttoni; Turppa, Tuomas; Hyyppä, Hannu; Hyyppä, Juha

doi:10.3390/ijgi6080243

Cited by 16 publications

(12 citation statements)

References 90 publications

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“…There are some positive initiatives to use LiDAR data nationwide in the future as topographic core data [17]. From 2015 Geiger mode LiDAR data is also available for commercial use, which can produce more accurate and denser point clouds much faster (even 1000 km 2 /hour) than the conventional linear LiDAR [18].…”

Section: Fig 2 the Sample Area (Google Earth)mentioning

confidence: 99%

Automatic selection of potential WWII bombed areas by using spatial data

Juhász

Neuberger

2018

Pollack Periodica

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The reconstruction of military defense systems, (e.g. World War II defense lines) is generally based on military object identification and mapping. Since unexploded bombs can be still dangerous today, detecting bomb craters can be useful in creating hazard maps. The most significant problem is managing the large amount of relevant data. Therefore, there is a strong demand for automatically select the potential danger zones and also automate the entire processing workflow. Automatic methods have been developed to reduce the areas of interest. In addition, this method can be applied in other tasks as well, for example in settlement detection.

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Section: Fig 2 the Sample Area (Google Earth)mentioning

confidence: 99%

Automatic selection of potential WWII bombed areas by using spatial data

Juhász

Neuberger

2018

Pollack Periodica

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“…Three-dimensional point cloud data are increasingly relevant in the context of several (emerging) applications such as digital urban and environmental twins, Building Information Modeling (BIM), autonomous driving, city modeling, and many others (Virtanen et al, 2017). One of the advantages of point clouds is the possibility to generate these data automatically for large areas and, thus, to enable frequent updates.…”

Section: Introductionmentioning

confidence: 99%

Integration of 3d Point Clouds With Semantic 3d City Models – Providing Semantic Information Beyond Classification

Beil

Kutzner

Schwab

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. A range of different and increasingly accessible acquisition methods, the possibility for frequent data updates of large areas, and a simple data structure are some of the reasons for the popularity of three-dimensional (3D) point cloud data. While there are multiple techniques for segmenting and classifying point clouds, capabilities of common data formats such as LAS for providing semantic information are mostly limited to assigning points to a certain category (classification). However, several fields of application, such as digital urban twins used for simulations and analyses, require more detailed semantic knowledge. This can be provided by semantic 3D city models containing hierarchically structured semantic and spatial information. Although semantic models are often reconstructed from point clouds, they are usually geometrically less accurate due to generalization processes. First, point cloud data structures / formats are discussed with respect to their semantic capabilities. Then, a new approach for integrating point clouds with semantic 3D city models is presented, consequently combining respective advantages of both data types. In addition to elaborate (and established) semantic concepts for several thematic areas, the new version 3.0 of the international Open Geospatial Consortium (OGC) standard CityGML also provides a PointCloud module. In this paper a scheme is shown, how CityGML 3.0 can be used to provide semantic structures for point clouds (directly or stored in a separate LAS file). Methods and metrics to automatically assign points to corresponding Level of Detail (LoD)2 or LoD3 models are presented. Subsequently, dataset examples implementing these concepts are provided for download.

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“…In indoor environments (e.g., Reference [7]), the semantic point cloud data is commonly associated with robotics, and often includes identification of individual objects. Finally, the possibility of associating IDs of individual objects (e.g., building IDs from Geographical Information Systems ) with point cloud segments has been proposed [8].…”

Section: Introductionmentioning

confidence: 99%

A Simple Semantic-Based Data Storage Layout for Querying Point Clouds

El-Mahgary

Virtanen

Hyyppä

2020

IJGI

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The importance of being able to separate the semantics from the actual (X,Y,Z) coordinates in a point cloud has been actively brought up in recent research. However, there is still no widely used or accepted data layout paradigm on how to efficiently store and manage such semantic point cloud data. In this paper, we present a simple data layout that makes use the semantics and that allows for quick queries. The underlying idea is especially suited for a programming approach (e.g., queries programmed via Python) but we also present an even simpler implementation of the underlying technique on a well known relational database management system (RDBMS), namely, PostgreSQL. The obtained query results suggest that the presented approach can be successfully used to handle point and range queries on large points clouds.

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Nationwide Point Cloud—The Future Topographic Core Data

Cited by 16 publications

References 90 publications

Automatic selection of potential WWII bombed areas by using spatial data

Automatic selection of potential WWII bombed areas by using spatial data

Integration of 3d Point Clouds With Semantic 3d City Models – Providing Semantic Information Beyond Classification

A Simple Semantic-Based Data Storage Layout for Querying Point Clouds

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