Examination arrangement

Course content

Teaching in lecture room, field data collection and labor experiments.

Learning outcome

Knowledge:

The candidate should have knowledge of:

• the working principle of a laser scanner, the classifications and parameter settings, the error sources and calibrations
• the differences of laser scanning and photogrammetry
• the data format and characteristics of Laser scanning point clouds
• the process of data preprocessing including noise filtering, strip adjustment and data registration
• principles and algorithms of classifications and segmentations, and the object detection as well

Skills:

The candidate is able to:

• process the raw data acquired directly from an airborne laser scanner and terrestrial laser scanner
• model fittings by using RANSAC or improved RANSAC
• merge point clouds acquired from UAV borne and terrestrial laser scanner by using different registration methods
• adapt and apply filters from digital image process for 3D point clouds
• detect buildings, trees, street lights and power lines from 3D point clouds
• modelling the detected objects in CityGML

General competence:

The candidate can:

• understand the relations and transformations among different coordinate system induced by the sensors (GNSS, INS, Laser transmitter, scanner)
• classify 3D point clouds into several classes (bare earth, cars, buildings and other man-made objects, power lines, pole-like objects)
• segment the 3D point clouds of a building into individual plane or curved facets and model them in 3D either by using data-driven method or model-driven method
• generate CityGML data files for the 3D objects
• understand and use professional terminology within the discipline
• work independently and in team and take the necessary initiatives
• identify common fields between this discipline and other professional disciplines and be open for inter disciplinary approach and cooperation

Learning methods and activities

Teaching in lecture room, field data collection and labor experiments.

Further on evaluation

The students will be given one project with individual task and data under the same topic. Students will be asked to develop their own algorithm based on the knowledge taught during the lecture and implement their algorithm for experimental test. A final report together with digital results are expected for the final evaluation.

Portfolio assessment is the basis for the grade in the course. The portfolio includes a report (70 %) and evaluation of two exercises (30 %). The results for the parts are given in %-scores, while the entire portfolio is assigned a letter grade. For a re-take of an examination, all assessments during the course must be re-taken

Recommended previous knowledge

Based on the course TBA4236 Theoretical Geomatics. Students need to have basic programming skills either in Matlab or in Python.

Course materials

G. Vosselman and H. Maas: Airborne and terrestrial Laser Scanning.