Using Graph Neural Networks to refine generated road networks. GNNs perform node classification, to remove invalid verties such as vertices on buildings, water bodies and greeneries.
Imitation learning explores the most generates the most roads however, it also generates the most invalid vertices especially on water bodies. RNGDet is used a the example here.
Magenta represents valid points , red points represents invalid points. Cyan represents road network.
Magenta represents predicted valid points , red points represents predicted invalid points. Cyan represents road network.
The following requires th road networks generated from RNGDet, and then the enerated dataset can be used for training this GNN, please replace the paths in respective filepaths. Some of hte pre trained models and datasets are still behind license since this as done as a part of my master thesis at DLR (German Aerospace Center) Oberpfaffenhofen.
- Ensure to have run all steps in RNGDetplusplus repo.
- Ensure to have downloaded the dataset and kept in
RNGDetPlusPlus\dataset\folder. - Ensure to have the restuls of the run of RNGDetplusplus in:
RNGDetPlusPlus/RNGDet_multi_ins/test
- Install all required dependencies using
conda env create --name gnn --file environment.yml
- Run
python gen_graph_data.py.
- Run
python gnn_correction.pyto construct and generate the dataset from the RNGDetplusplus results.
- The required dataset and parameters can be tuned in the code for now.
- Results are stored in the folder
Reports/
- Run again
python gnn_correction.pyto correct dataset graph from the RNGDetplusplus results.(Note that, you may choose to comment out the construction and storing of dataset since you have already generated it) - Results along with metrics and visualizations will be stored in the folder
Results_Correction/