Department of Computer Science
Maximilian Johenneken
Research Associate/ Project Garrulus/UAV Lab Coordination
Unit
Department of Computer Science, Institute of Technology, Resource and Energy-efficient Engineering (TREE)
Research fields
- Unmanned Aerial Systems / Drohnen
- Machine learning / Deep Learning
- Computer Vision
- Forestry / Agriculture
You can book appointments for contact hours via Calendly (link at the bottom)
Location
Sankt Augustin
Room
A 035
Address
Grantham-Allee 20
53757, Sankt Augustin
Contact hours
Mi: 13:00 - 17:00
Telephone
02241 865 95 84Research Projects
The Garrulus project aims to develop a fast, reliable and cost-effective method for the reforestation and monitoring of damaged forest areas in Germany. The automated direct seeding of trees offers a good opportunity to grow site-adapted and resilient trees. To this end, the sowing should be targeted at locations with the best possible conditions. Technologies such as drones and artificial intelligence should make the efficiency and effectiveness of direct seeding viable through precise seed planning, targeted drone seeding and subsequent regeneration monitoring.
Project management at the H-BRS
Prof. Dr Alexander Asteroth Prof. Dr Sebastian Houben
Publications
Johenneken, M., Drak, A., Mulye, M., Gharaibeh, T., Asteroth, A. (2023). Towards Multi-class Forest Floor Analysis. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13644. Springer, Cham. https://doi.org/10.1007/978-3-031-37742-6_20
M. Johenneken, A. Drak, R. Herpers and A. Asteroth, "Multimodal Segmentation Neural Network to Determine the Cause of Damage to Grasslands," 2021 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 2021, pp. 1-6, doi: 10.23919/SoftCOM52868.2021.9559072.
M. Johenneken, A. Drak and R. Herpers, "Damage Analysis of Grassland from Aerial Images Applying Convolutional Neural Networks," 2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 2020, pp. 1-6, doi: 10.23919/SoftCOM50211.2020.9238230.
M. K. Vasić et al., "Deep Semantic Image Segmentation for UAV-UGV Cooperative Path Planning: A Car Park Use Case," 2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 2020, pp. 1-6, doi: 10.23919/SoftCOM50211.2020.9238313.