DSMNet

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Height Prediction and Refinement from Aerial Images with Semantic and Geometric Guidance

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## DSMNet

### About
This repo contains the code and files necessary to reproduce the results published in our [paper](https://arxiv.org/pdf/2011.10697.pdf) 'Height Prediction and Refinement from Aerial Images with Semantic and Geometric Guidance'. Our method relies on a two stage pipeline : First, a multi-task network is used to predict the height, semantic labels and surface normals of an input RGB aerial image. Next, we use a denoising autoencoder to refine our height prediction in order to produce higher quality height maps. Training and testing is conducted on two publicly available datasets : The ISPRS Vaihingen and the IEEE DFC2018.

![](https://raw.githubusercontent.com/melhousni/DSMNet/main/images/fullnet.png)

### Citation
If you find our work useful in your research, please consider citing our [paper](https://arxiv.org/pdf/2011.10697.pdf):

```
@misc{mahdi2020height,
      title={Height Prediction and Refinement from Aerial Images with Semantic and Geometric Guidance}, 
      author={Elhousni Mahdi and Huang Xinming and Zhang Ziming},
      year={2020},
      eprint={2011.10697},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

```

## Network
### Prerequisites

* Python 3.5
* Tensorflow 2.1 (with Cuda 10.0)
* Numpy 1.18.4

### Testing
Both datasets can be found [here](https://drive.google.com/file/d/1llKA6z5L6CBQA5Fyr92d0Alj9TjhpYWb/view?usp=sharing). The data was organized and seperated into tiles to speed up the training process. No further pre-processing was done. Our checkpoints can be found [here](https://drive.google.com/file/d/1DkMmK1zvypZjqZ9GsIWqtdAO0ocEnTOQ/view?usp=sharing).

When unzipping the datasets and checkpoints, make sure to respect the following folder structure :  
  
/   
-datasets  
--DFC2018  
---RGB  
---SEM  
---DSM  
---DEM  
--Vaihingen  
---RGB  
---SEM  
---NDSM  
-checkpoints  
--DFC2018  
--Vaihingen

Next, use the test_dsm.py script to test the height prediction and refinement networks by using :  
**python test_dsm.py [dataset] [refinement_flag]**  
For example, to test the results of the prediction and refinement networks combined on the DFC2018 dataset, use :  
python test_dsm.py DFC2018 True  
To test the results of the prediction network only on the Vaihingen dataset, use :  
python test_dsm.py Vaihingen False  
The output files will be saved to the /output folder.

### Training
To train your own MTL prediction network, use:  
**python train_mtl.py [dataset]**  
For example, to train the MTL prediction network on the DFC2018 dataset, use :    
python train_mtl.py DFC2018   
  
<p align="center">
<img src="/images/mtl_output.png" width="500" height="400"/>  
</p>

To train your own refinement network, first you'll need a checkpoint for the MT prediction network, then you can use:  
**python train_ec.py [dataset]**    
For example, to train the refinement network on the Vaihingen dataset, use :    
python train_ec.py Vaihingen

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