veg-height-map-public

[SRS 2023] Countrywide Vegetation Height Estimation with Sentinel-2 and Deep Learning

🌲 Data Availability 🌲

The generated vegetation height maps of Switzerland (including both the mean & max height) are public accessible please download here

• resolution: 10m-resolution
• temporal coverage: 2017, 2018, 2019, 2020
• spatial coverage: the whole Switzerland

🌿 Requirements

• Python 3.8.5
• PyTorch: 1.7.1+cu110 (gcc/6.3.0, cudnn/8.0.5, cuda/11.0.3)
• HDF5/1.10.1
• GDAL/3.1.2

🌳 Preproccessing

• generate image stats

python -m scripts.calculate_stats --img=True --preproconfig=configs/preprocess.yaml

• preprocess images

python -m scripts.preprocess_img --preproconfig=configs/preprocess.yaml

• normalise images/labels

python -m scripts.calculate_stats --preproconfig=configs/train_spyr.yaml

• normalise DTM

python -m scripts.calculate_stats --preproconfig=configs/train_spyr.yaml --dtm True

🍀 Training

• train with DTM

python -m scripts.train --config=configs/train_spyr.yaml

• train without DTM

python -m scripts.train --config=configs/train_spyr_nd.yaml

🪴 Inference

• predict tile TMS with the model with DTM

python -m scripts.predictSet --pred_config_path=configs/predict_dtm_TMS.yaml --train_config_path=configs/train_spyr.yaml

• predict tile TMS with the model without DTM

python -m scripts.predictSet --pred_config_path=configs/predict_nodtm_TMS.yaml --train_config_path=configs/train_spyr_nd.yaml

🌵 Evaluation

python -m scripts.eval --config=configs/eval.yaml

🌱 Citation 🌱

@article{jiang2023accuracy,
  title={Accuracy and consistency of space-based vegetation height maps for forest dynamics in alpine terrain},
  author={Jiang, Yuchang and R{\"u}etschi, Marius and Garnot, Vivien Sainte Fare and Marty, Mauro and Schindler, Konrad and Ginzler, Christian and Wegner, Jan D},
  journal={Science of Remote Sensing},
  pages={100099},
  year={2023},
  publisher={Elsevier}
}