[SRS 2023] Countrywide Vegetation Height Estimation with Sentinel-2 and Deep Learning
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
- 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
- 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
- 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
- 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
python -m scripts.eval --config=configs/eval.yaml
@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}
}