TomoSAR2Height

This repository provides the code for training and evaluating TomoSAR2Height, a method for reconstructing building heights (nDSMs) from spaceborne TomoSAR point clouds.

🛠️ Installation

• Clone the repository

git clone git@github.com: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==0.18.0

🚀 Usage

📂 Data preparation

Prepare 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=munich

🎯 Training

Train 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=0

📊 Evaluation

Before 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=0

Specify run_suffix={YOUR_SUFFIX} with your desired suffix if needed. The results will be saved at ./outputs/TomoSAR2Height-{dataset}{run_suffix}/tiff_test.

⚙️ Available configurations

# check available configurations for training
python train.py --cfg job

# check available configurations for evaluation
python test.py --cfg job

Alternatively, review the configuration file: conf/config.yaml.

🎓 Citation

If you use TomoSAR2Height in a scientific work, please cite the paper:

@article{chen2026tomosar2height,
  author={Chen, Zhaiyu and Wang, Yuanyuan and Shi, Yilei and Zhu, Xiao Xiang},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Reconstructing Building Height from Spaceborne TomoSAR Point Clouds Using a Dual-Topology Network}, 
  year={2026},
  volume={},
  number={},
  pages={1-1},
  keywords={Point cloud compression;Buildings;Image reconstruction;Three-dimensional displays;Synthetic aperture radar;Optical scattering;Optical imaging;Optical interferometry;Radar polarimetry;Image resolution;Height estimation;3D reconstruction;SAR tomography;point cloud;deep learning},
  doi={10.1109/TGRS.2026.3656340}
}