2017
DOI: 10.3390/ijgi6080243
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Abstract: Topographic databases maintained by national mapping agencies are currently the most common nationwide data sets in geo-information. The application of laser scanning as source data for surveying is increasing. Along with this development, several analysis methods that utilize dense point clouds have been introduced. We present the concept of producing a dense nationwide point cloud, produced from multiple sensors and containing multispectral information, as the national core data for geo-information. Geo-info… Show more

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Cited by 16 publications
(7 citation statements)
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References 87 publications
(117 reference statements)
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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%
“…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%
“…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%
“…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%