Information Technology/Remote Sensing Division

Project:” Possible uses of simultaneously captured airborne

Laser scanning data and multispectral aerial images to capture damage

in hardwood (AirLaserSpec)”

Staffing as soon as possible, limited to 30.06.2027, remuneration according to grade 13 TV-L

The AirLaserSpec research project is being carried out against the backdrop of hardwood damage in the forests of northern Bavaria. Based on simultaneously collected aircraft-based laser scanner measurements and multispectral aerial image data, methods that are as efficient as possible for recording hardwood damage should be developed.

Your tasks:

• Development of methods for the automated recording of hardwood damage from simultaneously acquired airborne laser scanning data and multispectral aerial images

• Development of methods for automated recording of the extent and condition of rejuvenation in gaps in

Hardwood stocks from simultaneous flight data (laser scanning and multispectral aerial images)

• Literature review on recording hardwood damage and rejuvenation using remote sensing techniques

• Stereo aerial image interpretation: recording of damage symptoms from the digital 3D aerial photo to train automated classification methods and verify the results

• Field recordings in the project areas to create an interpretation key for stereo

Aerial photo interpretation

• Assessment of the practicality of the developed methods

• Preparation of publications and presentation of results at conferences

• Reporting and project management collaboration

Requirements:

• University degree (master/diploma) in forest sciences, geosciences or comparable courses of study with a focus on remote sensing and photogrammetry

• Experience in carrying out scientific studies in the field of remote sensing and vegetation analysis with proven publishing activity

• Experience in evaluating 3D point clouds (ideally from airplane-based laser scanner measurement) and optical remote sensing data

• Profound knowledge in the area of machine learning

• Experience in digital 3D stereo aerial image interpretation (ideally with a forest background)

• Profound knowledge of R and Python

• Very good ArcGIS desktop skills

• Experience with SURE, LasTools & Summit Evolution software is an advantage

• Willingness to engage in project management

• Organizational talent and ability to work independently

• Very good oral and written language skills in German and English

• Very high level of commitment, resilience & ability to work in a team

We offer:

• Collaboration in a varied, interesting, up-to-date and dynamic research environment

• comprehensive training and good working atmosphere, opportunities for personal development

• a well-equipped personal workplace; sufficient parking spaces, connection to public transport

• flexible and family-friendly working time arrangements with flexitime and the option to work from home

• Possibility to use many of TUM's offerings (e.g. canteen, university sports, etc.)