Organizers: Juha Hyyppä, Gottfried Mandlburger, Fabio Remondino, Narges Ttakhtkeshha

Light Detection and Ranging (LiDAR) is a well-established active technology for the direct acquisition of 3D data. In recent years, the emergence of Multispectral LiDAR (MSL) systems, which operate with two or more wavelengths, is revolutionizing the simultaneous acquisition of height and intensity information. MSL sensor properties (e.g., wavelength, instrument size and measurement range) are selected with respect to the intended application and domain which vary from forestry mapping, Land Use Land Cover (LULC) classification or change detection to bathymetry, topographic mapping, archaeology and geology.

MSL sensors provide information on the full 3D distribution of materials with improved penetration capacity hence they are becoming a valuable solution for geospatial data acquisition for NMCAs.

This online EuroSDR workshop on Multispectral LiDAR aims to review the emerging MSL and its possible application, with technical presentations and best practices from companies and mapping agencies.

Preliminary program of the workshop:

The workshop will begin with a welcome and introduction, setting the stage for a day of insightful discussions. This will be followed by a presentation on Multispectral LiDAR: technology review, processing methods and applications by Narges Takhtkeshha (FBK/TUW). The agenda continues with a series of experience-sharing sessions, starting with contributions from the R&D sector, followed by insights from two companies and two National Mapping and Cadastral Agencies (NMCAs).  The day will conclude with a wrap-up and final remarks, summarising key takeaways and next steps.

Please register below - a link to the online meeting will be shared with registered participants during the week of 16 - 20 June.  

References:

•  Takhtkeshha, N., Mandlburger, G., Remondino, F., Hyyppä, J., 2024: Multispectral Light Detection and Ranging Technology and Applications: A Review. Sensors; 24(5):1669
• Taher, J., Hyyppä, E., Hyyppä, M. et al., 2025: Multispectral airborne laser scanning for tree species classification: a benchmark of machine learning and deep learning algorithms. arXiv:2504.14337
• Ruoppa, L., Oinonen, O., Taher, J. et al., 2025: Unsupervised deep learning for semantic segmentation of multispectral LiDAR forest point clouds.   arXiv:2502.06227