Estimating Canopy Height at Scale
Authors: Jan Pauls, Max Zimmer, Una M. Kelly, Martin Schwartz, Sassan Saatchi, Philippe Ciais, Sebastian Pokutta, Martin Brandt, Fabian Gieseke
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A comparison between predictions and ground-truth labels yields an MAE / RMSE of 2.43 / 4.73 (meters) overall and 4.45 / 6.72 (meters) for trees taller than five meters, which depicts a substantial improvement compared to existing global-scale maps. We conduct several ablation studies to evaluate the impact of the involved model components. |
| Researcher Affiliation | Academia | 1Department of Information Systems, University of M unster, Germany 2Department for AI in Society, Science, and Technology, Zuse Institute Berlin, Germany 3Laboratoire des Sciences du Climat et de l Environnement, LSCE/IPSL, France 4Jet Propulsion Laboratory (JPL), California Institute of Technology, USA 5Department of Geosciences and Natural Resource Management, University of Copenhagen, Denmark 6Department of Computer Science, University of Copenhagen, Denmark. |
| Pseudocode | No | The paper describes the approach and model architecture in text and figures but does not provide pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our source code, as well as detailed documentation, are publicly available on Git Hub.6 |
| Open Datasets | Yes | For the Sentinel-1 data, we adopt a methodology similar to that used by Schwartz et al. (2024)... For the Sentinel-2 data... we resort to a cloud reduction algorithm adapted from Braaten (2024)... As ground-truth labels, we resort to the data provided by the GEDI mission. In particular, we make use of the so-called RH100 metric... To mitigate the impact induced by incorrect canopy height values in mountainous areas, we resort to data from the Shuttle Radar Topography Mission (SRTM)... |
| Dataset Splits | Yes | Overall, 100 000 patches are extracted, out of which 80% are used for training, 10% for validation, and 10% for testing. The validation set was used for hyperparameter selection. |
| Hardware Specification | Yes | The computational process consumes approximately 1 500 GPU hours on a GPU cluster with various GPU devices (e.g., Nvidia RTX3090s and A100s). We also appreciate the hardware donation of an A100 Tensor Core GPU from Nvidia. |
| Software Dependencies | No | The paper mentions architectural components and optimizers (U-Net, ResNet-50, AdamW, Huber loss) but does not provide specific software version numbers for dependencies like Python, PyTorch, or TensorFlow. |
| Experiment Setup | Yes | We optimize the model weights using the Adam W (Loshchilov & Hutter, 2017) optimizer with a weight decay of 0.001, a batch size of 32, and an initial learning rate of 0.001. We also resort to a linear learning rate warm-up for the first 10% of the iterations and a linear learning rate scheduler for the remaining 90%. Additionally, gradient clipping was applied to prevent gradient explosion. Finally, the shifted Huber loss was used during training, i.e., LS with the Huber loss as pixel-wise loss function L in Equation (4). |