AI-Based High-Resolution Forest Monitoring ongoing

Preserving global vegetation is crucial for addressing and mitigating climate change. Accurate, up-to-date forest health data is essential. AI4Forest aims to develop advanced AI methods to monitor forests using satellite imagery, including radar and optical data. The project will create scalable techniques for detailed, high-resolution maps of the globe, e.g., to monitor canopy height, biomass, and to track forest disturbances.

AI4Forest
Jun 2023 to May 2027
3
4

🧑‍🎓 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

Accurate and up-to-date information on global forest health, disturbances, evolution of above ground biomass and carbon storage is essential for policy-makers to design effective strategies for climate adaptation and mitigation. Advances in Earth observation and AI have paved the way for automated forest monitoring using satellite data, along with radar and LiDAR data. However, existing forest maps often have low resolution, cover limited areas or fail to track temporal dynamics.

The AI4Forest project brings together experts in AI, Applied Mathematics, Computer Science, Remote Sensing, and Climate Science, being 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 regular updates across Europe and globally.

Estimating Canopy Height at Scale

Recently, we released a global canopy height map with a 10m resolution, based on satellite data from 2020. You can find the paper on arXiv. Below is an image of the height map, you can explore the interactive global map on the Google Earth Engine.

A global canopy height map at a 10 m resolution.

The computational pipeline to produce the height map can be visualized as follows:
Computational pipeline for the height map.

Comparing our map with two other existing global height maps (Lang et al., Potapov et al.), as well as a regional map for France, shows a significant improvement in visual quality.
Comparison with existing maps.

Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation

Going a step further, we also built a model that takes the temporal evolution of canopy height over multiple years into account. A crucial step in this process, which we describe in our paper, is to use 12 images per year (i.e., one image per month) as input, as opposed to a median-composite. While this is computationally more expensive and could include more clouds, the model is able to capture seasonal variations as well as geolocation shifts. While we provide the first map at 10m resolution for the entire european continent and for years 2019-2022, our model is able to estimate canopy height much better, as the following comparison to the ALS ground truth shows. Comparison with ALS ground truth.

Restricting our model to predicting only the height map for the year 2020, we can compare the results to other maps for the same year, including our previous map. We are able to significantly improve both the visual quality as well as the accuracy of the map. Comparison with other maps for the year 2020.

💬 Talks and posters

Poster presentations

Jul 2024
Estimating Canopy Height at Scale by Max Zimmer
41st International Conference on Machine Learning (ICML), Vienna
May 2024
Sparse Model Soups a Recipe for Improved Pruning Via Model Averaging by Max Zimmer
12th International Conference on Learning Representations (ICLR), Vienna
May 2023
How I Learned to Stop Worrying and Love Retraining by Max Zimmer
11th International Conference on Learning Representations (ICLR), Kigali
Mar 2023
How I Learned to Stop Worrying and Love Retraining by Max Zimmer
Workshop on Optimization and Machine Learning, Waischenfeld

📝 Publications and preprints

Preprints

  1. Pauls, J., Zimmer, M., Turan, B., Saatchi, S., Ciais, P., Pokutta, S., and Gieseke, F. (2025). Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation. [arXiv]
    [BibTeX]
    @misc{2025_JanEtAl_Temporalcanopyheight,
  archiveprefix = {arXiv},
  eprint = {2501.19328},
  primaryclass = {cs.LG},
  year = {2025},
  author = {Pauls, Jan and Zimmer, Max and Turan, Berkant and Saatchi, Sassan and Ciais, Philippe and Pokutta, Sebastian and Gieseke, Fabian},
  title = {Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation},
  date = {2025-01-31}
}

Conference proceedings

  1. Zimmer, M., Spiegel, C., and Pokutta, S. (2023, June 29). Sparse Model Soups: A Recipe for Improved Pruning Via Model Averaging. Proceedings of the International Conference on Learning Representations. [URL] [arXiv]
    [BibTeX]
    @inproceedings{2023_ZimmerSpiegelPokutta_Sparsemodelsoups,
  year = {2024},
  booktitle = {Proceedings of the 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},
  date = {2023-06-29}
}
  2. Pauls, J., Zimmer, M., Kelly, U. M., Schwartz, M., Saatchi, S., Ciais, P., Pokutta, S., Brandt, M., and Gieseke, F. (2024, June 3). Estimating Canopy Height at Scale. Proceedings of the International Conference on Machine Learning. [arXiv] [code]
    [BibTeX]
    @inproceedings{2024_PaulsEtAl_Canopyheightestimation,
  year = {2024},
  booktitle = {Proceedings of the 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},
  date = {2024-06-03}
}
  3. Zimmer, M., Spiegel, C., and Pokutta, S. (2021, November 1). How I Learned to Stop Worrying and Love Retraining. Proceedings of the International Conference on Learning Representations. [URL] [arXiv] [code]
    [BibTeX]
    @inproceedings{2021_ZimmerSpiegelPokutta_Retrainingpruning,
  year = {2023},
  booktitle = {Proceedings of the 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},
  date = {2021-11-01}
}