Data mining for classification of high volume dense lidar data in an urban area

Download statistics - Document (COUNTER):

Chauhan, I.; Brenner, Claus: Data mining for classification of high volume dense lidar data in an urban area. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences IV-5 (2018), S. 391-395. DOI:

Repository version

To cite the version in the repository, please use this identifier:

Selected time period:


Sum total of downloads: 61

3D LiDAR point cloud obtained from the laser scanner is too dense and contains millions of points with information. For such huge volume of data to be sorted, identified, validated and be used for prediction, data mining provides immense scope and has been used to achieve the same. Certain unique attributes were selected as an input for creating models through machine learning. Supervised models were thus built for prediction of classes through the available LiDAR data using random forest algorithm. The algorithm was chosen owing to its efficiency and accuracy over other data mining algorithms. The models created using random forest were then tested on an unclassified point cloud data of an urban area. The method shows promising results in terms of classification accuracy as overall accuracy of 91.71 % was achieved for pixel-based classification. The method also displays enhanced efficiency over common classification algorithms as the time taken to make predictions about the data is reduced considerably for a set of dense LiDAR data. This shows positive foresight of making use of data mining and machine learning to handle large volume of LiDAR data and can go a long way in augmenting efficient processing of LiDAR data.
License of this version: CC BY 4.0 Unported
Document Type: article
Publishing status: publishedVersion
Issue Date: 2018
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

distribution of downloads over the selected time period:

downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 38 62.30%
2 image of flag of India India 7 11.48%
3 image of flag of Korea, Republic of Korea, Republic of 5 8.20%
4 image of flag of Estonia Estonia 3 4.92%
5 image of flag of United States United States 2 3.28%
6 image of flag of Malaysia Malaysia 1 1.64%
7 image of flag of Croatia Croatia 1 1.64%
8 image of flag of Chile Chile 1 1.64%
9 image of flag of Switzerland Switzerland 1 1.64%
10 image of flag of Brazil Brazil 1 1.64%
    other countries 1 1.64%

Further download figures and rankings:


Zur Erhebung der Downloadstatistiken kommen entsprechend dem „COUNTER Code of Practice for e-Resources“ international anerkannte Regeln und Normen zur Anwendung. COUNTER ist eine internationale Non-Profit-Organisation, in der Bibliotheksverbände, Datenbankanbieter und Verlage gemeinsam an Standards zur Erhebung, Speicherung und Verarbeitung von Nutzungsdaten elektronischer Ressourcen arbeiten, welche so Objektivität und Vergleichbarkeit gewährleisten sollen. Es werden hierbei ausschließlich Zugriffe auf die entsprechenden Volltexte ausgewertet, keine Aufrufe der Website an sich.

Search the repository