AI-Based High-Resolution Forest Monitoring ongoing

Preserving forests is crucial for climate adaptation and mitigation. Accurate, up-to-date forest health data is essential. AI4Forest aims to develop advanced AI methods to monitor forests using satellite, radar, and LiDAR data. The project will create scalable techniques for detailed, high-resolution forest maps, updated weekly across Europe and globally.

AI4Forest
Jun 2023 to May 2027
3
3

🧑‍🎓 Project Members (excluding external)

Sebastian Pokutta
Principal Investigator
pokutta (at) zib.de
Berkant Turan
turan (at) zib.de
Max Zimmer
zimmer (at) zib.de

🪙 Funding

This project is being funded by the Federal Ministry of Education and Research and the German Aerospace Center (project ID 01IS23025B) from June 2023 to May 2027.

🔬 Project Description

The AI4Forest project focuses on the preservation of ecosystems, particularly forests, as a key component of climate adaptation and protection strategies. Accurate and up-to-date information on forest health and carbon balance is essential to assess the current state of forests and implement effective countermeasures against deforestation. Advances in Earth observation and artificial intelligence have paved the way for automated forest monitoring using satellite data, along with radar and LiDAR data. However, existing forest maps often have low resolutions or cover limited areas. A global canopy height map at a 10 m resolution. AI4Forest brings together experts in artificial intelligence, applied mathematics, computer science, remote sensing, and climate change and is a collaboration between ZIB, LSCE, CNRS, WWU and TUM. The project aims to develop scalable AI methods for forest monitoring and to efficiently and cost-effectively process large datasets. These advancements will enable the creation of detailed forest maps with high spatial and temporal resolution, down to individual trees and species, and allow for their weekly updates across Europe and globally.

💬 Talks and posters

Poster presentations

May 2023
How I Learned to Stop Worrying and Love Retraining by Max Zimmer
11th ICLR Conference, Kigali

📝 Publications and preprints

  1. Pauls, J., Zimmer, M., Kelly, U. M., Schwartz, M., Saatchi, S., Ciais, P., Pokutta, S., Brandt, M., and Gieseke, F. (2024). Estimating Canopy Height at Scale. Proceedings of International Conference on Machine Learning. [arXiv] [code]
    [BibTeX]
    @inproceedings{canopy2024,
  year = {2024},
  booktitle = {Proceedings of International Conference on Machine Learning},
  archiveprefix = {arXiv},
  eprint = {2406.01076},
  primaryclass = {cs.CV},
  author = {Pauls, Jan and Zimmer, Max and Kelly, Una M and Schwartz, Martin and Saatchi, Sassan and Ciais, Philippe and Pokutta, Sebastian and Brandt, Martin and Gieseke, Fabian},
  title = {Estimating Canopy Height at Scale},
  code = {https://github.com/AI4Forest/Global-Canopy-Height-Map}
}
  2. Zimmer, M., Spiegel, C., and Pokutta, S. (2024). Sparse Model Soups: A Recipe for Improved Pruning Via Model Averaging. Proceedings of International Conference on Learning Representations. [URL] [arXiv]
    [BibTeX]
    @inproceedings{zsp_modelsoup_23,
  year = {2024},
  booktitle = {Proceedings of International Conference on Learning Representations},
  url = {https://iclr.cc/virtual/2024/poster/17433},
  archiveprefix = {arXiv},
  eprint = {2306.16788},
  primaryclass = {cs.LG},
  author = {Zimmer, Max and Spiegel, Christoph and Pokutta, Sebastian},
  title = {Sparse Model Soups: A Recipe for Improved Pruning Via Model Averaging}
}
  3. Zimmer, M., Spiegel, C., and Pokutta, S. (2023). How I Learned to Stop Worrying and Love Retraining. Proceedings of International Conference on Learning Representations. [URL] [arXiv] [code]
    [BibTeX]
    @inproceedings{zsp_retrain_21,
  year = {2023},
  booktitle = {Proceedings of International Conference on Learning Representations},
  url = {https://iclr.cc/virtual/2023/poster/10914},
  archiveprefix = {arXiv},
  eprint = {2111.00843},
  primaryclass = {cs.LG},
  author = {Zimmer, Max and Spiegel, Christoph and Pokutta, Sebastian},
  title = {How I Learned to Stop Worrying and Love Retraining},
  code = {https://github.com/ZIB-IOL/BIMP}
}