Hyperspectral dataset available as open data

Our novel Hyperspectral Terrestrial Laser Scanning (TLS) dataset has been published as open data.

The laser scanning point cloud dataset consists of 30 individual scans collected as a time series covering a 26 hour time frame. Each point in the laser scanning point cloud contains colour information from several different wavelengths. Ordinary laser scanning data has colour information from one wavelength.

Presentation of hyperspectral data

Hyperspectral data on a birch

 

Compared to traditional laser scanning data, hyperspectral data has advantages in for example target recognition and change detection, where it is possible to track changes of color of tree leaves, as in the data published. Also 3D RGB presentations can be produced from data without using separate camera. All laser scanners enable collecting data without external light source, also in the dark.

The dataset is available via the Etsin research data finding service provided by the Finnish Ministry of Education and Culture (http://urn.fi/urn:nbn:fi:csc-ida-4x201604052015015324658s). Datasets are provided in .laz format and published under the Creative Commons Attribution 4.0 International License (CC-BY-40).

The data has been utilised in recent research on movement of a birch during a 26 hour period, where it was found out that trees “sleep” during night. Click the picture below to see an animation on the movement of a tree:

Movement of a birch during 26 hour period

Movement of a birch during the night.

 

More information: Dr. Eetu Puttonen, eetu.puttonen(a)nls.fi

Read more on the published data at the National Land Survey and about the sleeping trees at the NLS and TU Wien press releases

 

 

 

Capturing Urban Data with Backpack Laser Scanning

Personal mobile mapping on a backpack (PLS) is a novel innovation from FGI. The application uses various technologies: GNSS-IMU positioning, laser scanning, digital photography and data driven algorithms for improving the positioning in often GNSS denied urban space. PLS allows rapid data collection of complex environment without compromising the data coverage, precision and accuracy of data. The approach is a flexible solution for varied situations and mapping tasks in urban space, and applicable to e.g. building façade reconstruction, street mapping, urban arboculture and change detection.
The research aims at development of modern surveying practices, investigates alternative system and sensor layouts and performance related issues as well as formulates automated data processes for 3D modeling and seeks for methods for improving geometric quality of data and data fusion.

More information: Dr. Antero Kukko, Antero.Kukko(a)nls.fi

 

Capturing urban data with PLS

Above: 3D geometry and imagery are collected simultaneously using backpack PLS providing an efficient tool for urban mapping.

 

Point cloud model of a building

Above: Point cloud data collected with backpack PLS captures building geometry fast in high details and accuracy.