Awesome

Abstract

Recently, road graph extraction has garnered increasing attention due to its crucial role in autonomous driving, navigation, etc. However, accurately and efficiently extracting road graphs remains a persistent challenge, primarily due to the severe scarcity of labeled data. To address this limitation, we collect a global-scale satellite road graph extraction dataset, i.e. Global-Scale dataset. Specifically, the Global-Scale dataset is $\sim20 \times$ larger than the largest existing public road extraction dataset and spans over 13,800 $km^2$ globally. Additionally, we develop a novel road graph extraction model, i.e. SAM-Road++, which adopts a node-guided resampling method to alleviate the mismatch issue between training and inference in SAM-Road, a pioneering state-of-the-art road graph extraction model. Furthermore, we propose a simple yet effective ``extended-line'' strategy in SAM-Road++ to mitigate the occlusion issue on the road. Extensive experiments demonstrate the validity of the collected Global-Scale dataset and the proposed SAM-Road++ method, particularly highlighting its superior predictive power in unseen regions.

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.
• 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 sam_road repo to download City-scale and SpaceNet datasets. Put them in the main directory, structure like:

-sam_road++  
--cityscale  
---20cities  
--spacenet  
---RGB_1.0_meter  

and run python generate_labes.py under both dirs.

for Global-scale, refer to cityscale for data preparation. And our Global-scale datasets will be publicly available soon.

Training

City-scale dataset:

python train.py --config=config/toponet_vitb_512_cityscale.yaml  

Glbale-scale dataset:

python train.py --config=config/toponet_vitb_512_globalscale.yaml

or

python train.py --config=config/toponet_vitb_256_globalscale.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  

Test

For APLS and TOPO metrics, please move to Sat2Graph. It is worth mentioning that the metrics used to test our Global-scale datasets are the same as those used for the Cityscale datasets.

Demos

Citation

@article{yin2024satelliteimageroadgraph,
  title={Towards Satellite Image Road Graph Extraction: A Global-Scale Dataset and A Novel Method},
  author={Yin, Pan and Li, Kaiyu and Cao, Xiangyong and Yao, Jing and Liu, Lei and Bai, Xueru and Zhou, Feng and Meng, Deyu},
  journal={arXiv preprint arXiv:2411.16733},
  year={2024}
}

Acknowledgement

We sincerely appreciate the authors of the following codebases which made this project possible:

• Segment Anything Model
• SAM_Road
• Sat2Graph
• SAMed
• Detectron2

TODO List

• [√] Basic instructions
• [×] Organize configs
• [√] Add dependency list
• [×] Add demos
• [×] Add trained checkpoints