https://github.com/htcr/sam_road
Segment Anything Model for large-scale, vectorized road network extraction from aerial imagery. CVPRW 2024
https://github.com/htcr/sam_road
autonomous-driving computer-vision cvpr2024 graph graph-neural-networks graph-representation-learning mapping navigation remote-sensing scene-graph scene-graph-generation segment-anything segmentation-models transformers
Last synced: 5 months ago
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Segment Anything Model for large-scale, vectorized road network extraction from aerial imagery. CVPRW 2024
- Host: GitHub
- URL: https://github.com/htcr/sam_road
- Owner: htcr
- License: mit
- Created: 2024-02-26T05:11:49.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-10T18:36:56.000Z (9 months ago)
- Last Synced: 2024-08-10T20:04:19.138Z (9 months ago)
- Topics: autonomous-driving, computer-vision, cvpr2024, graph, graph-neural-networks, graph-representation-learning, mapping, navigation, remote-sensing, scene-graph, scene-graph-generation, segment-anything, segmentation-models, transformers
- Language: Python
- Homepage: https://arxiv.org/pdf/2403.16051.pdf
- Size: 3.08 MB
- Stars: 117
- Watchers: 2
- Forks: 15
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Segment-Anything - [code
README
# Official codebase for "Segment Anything Model for Road Network Graph Extraction", CVPRW 2024
https://arxiv.org/pdf/2403.16051.pdf
[The paper](https://openaccess.thecvf.com/content/CVPR2024W/SG2RL/papers/Hetang_Segment_Anything_Model_for_Road_Network_Graph_Extraction_CVPRW_2024_paper.pdf) has been accepted by IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR) 2024, 2nd Workshop on Scene Graphs and Graph Representation Learning.
[Received the best paper award of the workshop](https://sites.google.com/corp/view/sg2rl/). Big thanks to the organizers for the recognition!!
## Demos
Predicted road network graph in a large region (2km x 2km).
Predicted road network graphs and corresponding masks in dense urban with complex and irregular structures.
## Installation
You need the following:
- an Nvidia GPU with latest CUDA and driver.
- the latest pytorch.
- pytorch lightning.
- wandb.
- Go, just for the APLS metric (we should really re-write this with pure python when time allows).
- and pip install whatever is missing.
## Getting Started
### SAM Preparation
Download the ViT-B checkpoint from the official SAM directory. Put it under:
-sam_road
--sam_ckpts
---sam_vit_b_01ec64.pth
### Data Preparation
Refer to the instructions in the RNGDet++ repo (https://github.com/TonyXuQAQ/RNGDetPlusPlus) to download City-scale and SpaceNet datasets.
Put them in the main directory, structure like:
-sam_road
--cityscale
---20cities
--spacenet
---RGB_1.0_meter
Download links copied from https://github.com/TonyXuQAQ/RNGDetPlusPlus
#### SpaceNet
https://drive.google.com/uc?id=1FiZVkEEEVir_iUJpEH5NQunrtlG0Ff1W
The data_split.json is copied from the dataset.json in this folder.
#### CityScale
https://drive.google.com/uc?id=1R8sI1RmFe3rUfWMQaOfsYlBDHpQxFH-H
Find the 20cities folder under this folder.
Then, run "python generate_labes.py" under both dirs.
### Training
City-scale dataset:
python train.py --config=config/toponet_vitb_512_cityscale.yaml
SpaceNet dataset:
python train.py --config=config/toponet_vitb_256_spacenet.yaml
You can find the checkpoints under lightning_logs dir.
### Inference
python inferencer.py --config=path_to_the_same_config_for_training --checkpoint=path_to_ckpt
This saves the inference results and visualizations.
Inferencing with our checkpoints:
Cityscale:
python inferencer.py --config=config/toponet_vitb_512_cityscale.yaml --checkpoint=/path_to/cityscale_vitb_512_e10.ckpt
Spacenet:
python inferencer.py --config=config/toponet_vitb_256_spacenet.yaml --checkpoint=/path_to/spacenet_vitb_256_e10.ckpt
### Test
Go to cityscale_metrics or spacenet_metrics, and run
bash eval_schedule.bash
Check that script for details. It runs both APLS and TOPO and stores scores to your output dir.
## Our Checkpoints
[congrui/sam_road](https://huggingface.co/congrui/sam_road)
## Citation
```
@article{hetang2024segment,
title={Segment Anything Model for Road Network Graph Extraction},
author={Hetang, Congrui and Xue, Haoru and Le, Cindy and Yue, Tianwei and Wang, Wenping and He, Yihui},
journal={arXiv preprint arXiv:2403.16051},
year={2024}
}
```
## Acknowledgement
We sincerely appreciate the authors of the following codebases which made this project possible:
- Segment Anything Model
- RNGDet++
- SAMed
- Detectron2
## TODO List
- [x] Basic instructions
- [x] Organize configs
- [x] Add dependency list
- [x] Add demos
- [x] Add trained checkpoints