This is the code repository for From Pixels to Progress: Generating Road Network from Satellite Imagery for Socioeconomic Insights in Impoverished Areas.
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pre-processing: identify the noisy or cloud covered satellite imagery.
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CoANet3: generate the road masks based on the CoANet model pretrained on the DeepGlobe dataset. Please refer to the original github repository for detailed description (CoANet: Connectivity Attention Network for Road Extraction From Satellite Imagery).
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post-processing: concat the road masks and perform morthological operations.
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topology_construction: transform the road skeleton image into the road network shapefile. Please refer to the original github repository for detailed description (Learning to Generate Maps from Trajectories).
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process_RN2.py: transform the road network shapefile into graph and calculate the structural features.
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sample_roadnetwork: an example of the extracted road network in one impoverished county. The whole dataset is too large to upload here. We are trying to upload them elsewhere.
Citation
If you find this project useful in your research, please consider citing:
@article{xi2024pixels,
title={From Pixels to Progress: Generating Road Network from Satellite Imagery for Socioeconomic Insights in Impoverished Areas},
author={Xi, Yanxin and Liu, Yu and Liu, Zhicheng and Tarkoma, Sasu and Hui, Pan and Li, Yong},
journal={arXiv preprint arXiv:2406.11282},
year={2024}
}