The Sustainable Development Goals (SDGs) aim to resolve societal challenges,
such as eradicating poverty and improving the lives of vulnerable populations
in impoverished areas. Those areas rely on road infrastructure construction to
promote accessibility and economic development. Although publicly available
data like OpenStreetMap is available to monitor road status, data completeness
in impoverished areas is limited. Meanwhile, the development of deep learning
techniques and satellite imagery shows excellent potential for earth
monitoring. To tackle the challenge of road network assessment in impoverished
areas, we develop a systematic road extraction framework combining an
encoder-decoder architecture and morphological operations on satellite imagery,
offering an integrated workflow for interdisciplinary researchers. Extensive
experiments of road network extraction on real-world data in impoverished
regions achieve a 42.7% enhancement in the F1-score over the baseline methods
and reconstruct about 80% of the actual roads. We also propose a comprehensive
road network dataset covering approximately 794,178 km2 area and 17.048 million
people in 382 impoverished counties in China. The generated dataset is further
utilized to conduct socioeconomic analysis in impoverished counties, showing
that road network construction positively impacts regional economic
development. The technical appendix, code, and generated dataset can be found
at
this https URL

通过结合编码 - 解码架构和卫星图像的形态学运算，我们开发了一个系统的道路提取框架，为跨学科研究人员提供了一种集成的工作流。在贫困地区的实际数据上进行的道路网络提取的大量实验证明，相比于基准方法，F1 得分提高了 42.7％，重建了大约 80％的实际道路。我们还提出了一个全面的道路网络数据集，涵盖了中国 382 个贫困县约 794,178 平方公里的面积和 17.048 万人口，利用这个生成的数据集进行了贫困县的社会经济分析，结果显示道路网络建设对地区经济发展有积极影响。