This repository provides the code for training and evaluating TomoSAR2Height, a method for reconstructing building heights (nDSMs) from spaceborne TomoSAR point clouds.
- Clone the repository
git clone [email protected]:zhu-xlab/tomosar2height.git
cd tomosar2height- All-in-one installation to create a conda environment with all dependencies
conda env create -f environment.yml && conda activate tomosar2height- If you prefer manual installation, follow these steps
conda create --name tomosar2height python=3.10
conda activate tomosar2height
conda install pytorch==2.3.0 torchvision==0.18.0 pytorch-cuda=11.8 pytorch-scatter affine laspy matplotlib rasterio scikit-learn scipy tabulate tqdm transformations trimesh urllib3 wandb hydra-core hydra-colorlog omegaconf gdal=3.6 -c pyg -c pytorch -c nvidia -c conda-forge
pip install open3d==0.18.0Prepare the dataset (about 10 seconds):
# Build the Berlin data
python scripts/build_dataset.py dataset=berlin
# Build the Munich data
python scripts/build_dataset.py dataset=munichTrain TomoSAR2Height using different data modalities:
- Using point clouds only
# Replace `berlin` to `munich` for Munich data
python train.py dataset=berlin use_cloud=true use_image=false wandb=true run_suffix=_cloud gpu_id=0- Using point clouds and images
# Replace `berlin` to `munich` for Munich data
python train.py dataset=berlin use_cloud=true use_image=true wandb=true run_suffix=_cloud+image gpu_id=0Before evaluation, make sure checkpoints are available at ./outputs/TomoSAR2Height-{dataset}{run_suffix}/check_points/model_best.pt.
- Evaluate a trained TomoSAR2Height model (point clouds only):
# Berlin data
python test.py dataset=berlin use_cloud=true use_image=false run_suffix=_cloud gpu_id=0
# Munich data
python test.py dataset=munich use_cloud=true use_image=false run_suffix=_cloud gpu_id=0- Evaluate a trained TomoSAR2Height model (point clouds and images):
# Berlin data (point cloud & image)
python test.py dataset=berlin use_cloud=true use_image=true run_suffix=_cloud+image gpu_id=0
# Munich data (point cloud & image)
python test.py dataset=munich use_cloud=true use_image=true run_suffix=_cloud+image gpu_id=0Specify run_suffix={YOUR_SUFFIX} with your desired suffix if needed. The results will be saved at ./outputs/TomoSAR2Height-{dataset}{run_suffix}/tiff_test.
# check available configurations for training
python train.py --cfg job
# check available configurations for evaluation
python test.py --cfg jobAlternatively, review the configuration file: conf/config.yaml.