RT Dissertation/Thesis
T1 From efficient airbone LIDAR data processing and classification to 3D point cloud visualisation
A1 Martínez Sánchez, Jorge
K1 Remote Sensing
K1 Airborne LiDAR
K1 point clouds
K1 segmentation
K1 classification
K1 ground filtering
K1 visualisation
AB The acquisition of knowledge about the world is an essential endeavour ofscience. However, performing on-site observations at a global scale is unfeasible, therefore remote sensing is anappealing alternative. Over the last two decades, LiDAR (Light Detection And Ranging), an active remote sensingtechnique, has gained significant adoption. LiDAR allows acquiring a 3D record of the target scene in the form ofpoint cloud with high accuracy. The goal of this thesis is to develop efficient methods for the classification of LiDARdata. For this, both general-purpose methods (segmentation and classification) and application-specific methods(building and road points extraction) are proposed, which have been efficiently implemented in a middle-to-low levellanguage with an optimal spatial indexing and multi-core parallelisation. Also, the feasibility of real-time groundfiltering is explored exploiting the scan-line acquisition pattern of the LiDAR data. This implementation was portedinto a development board using FPGA acceleration where experiments demonstrated that it can cope with the dataacquisition rates of the current lightweight scanners used in UAVs. Furthermore, a point cloud visualisation tool,namely OLIVIA, is presented. OLIVIA is an OpenGL-based open-source project implemented in Java, that offers aneasy way to create customised visualisation and the capability of 3D stereoscopic view.
YR 2020
FD 2020
LK http://hdl.handle.net/10347/24550
UL http://hdl.handle.net/10347/24550
LA eng
DS Minerva
RD 24 abr 2026