HTC-DC Net

This repository provides the implementation for HTC-DC Net, a network designed for monocular height estimation (nDSM) from single-view remote sensing images. For detailed methodology and results, please refer to our paper.

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🛠️ Installation

⚠️ Note: The provided environment file is for reference only. Some packages may require manual installation or configuration.

Recommended Environment

• pytorch 1.7.1
• pytorch3d 0.4.0

  ⚙️ PyTorch3D typically requires manual compilation with GPU support. Please follow the official installation instructions.

• fvcore 0.1.5
• timm 0.9.7
• scikit-image 0.21.0
• wandb (for experiment tracking)

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🚀 Usage

⚙️ Configuration Files

Two configuration files are needed to launch training:

1. Data & Logging Configuration
   See configs/configs1.yaml as an example. You must define:

   • data_dir: path to your dataset
   • data_split_dirs: directory containing train/val/test splits
   • test_data_split_dirs: directory with test splits
   • patch_size: patch size used by the transformer module (specific to HTC-DC Net)

2. Model & Training Configuration
   See configs/htcdc.yaml as a reference. Most hyperparameters are set as defaults in htcdc.py, but you may override them here if desired.

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📂 Data Preparation

Organize your dataset in the following structure under data_dir:

📂 data_dir
    📂 image # Opitcal satellite images
    📂 mask # Building footprint masks (optional, for computing building metrics)
    📂 ndsm (Ground truth normalized DSMs)

Each scene should have the same filename base, e.g., scene_001, with different suffixes:

• _IMG.tif – optical image
• _BLG.tif – building mask (optional)
• _AGL.tif – nDSM height map

Example:

scene_001_IMG.tif
scene_001_BLG.tif
scene_001_AGL.tif

Define your data splits in data_split_dirs as:

📂 data_split_dirs
    🖹 train.txt
    🖹 val.txt
    🖹 test.txt

Each file lists scene bases (without extensions), e.g.:

scene_001
scene_002
...
scene_xxx

In test_data_split_dirs, only test.txt is needed.

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🎯 Training

Start training with:

python train.py --config configs/configs1.yaml --exp_config configs/htcdc.yaml

This will save the configuration and checkpoints under a timestamped directory.

To resume training:

python train.py --exp_config /path/to/saved/config --restore

After training, there will be several checkpoint files under the checkpoint directory, checkpoint_last.pth.tar for the last epoch, checkpoint_best_rmse.pth.tar for the epoch with best validation RMSE, and so on.

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📊 Evaluation

Evaluate a trained model with:

python test.py --config /path/to/archived/config/under/checkpoint/directory test_checkpoint_file checkpoint_best_rmse.pth.tar

Replace checkpoint_best_rmse.pth.tar with any other saved checkpoint as needed. The results with be save as result_best_rmse.pth.tar.