  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
https://github.com/tsinghua-fib-lab/Road_network_extraction_impoverished_counties.
