  Cities play an important role in achieving sustainable development goals
(SDGs) to promote economic growth and meet social needs. Especially satellite
imagery is a potential data source for studying sustainable urban development.
However, a comprehensive dataset in the United States (U.S.) covering multiple
cities, multiple years, multiple scales, and multiple indicators for SDG
monitoring is lacking. To support the research on SDGs in U.S. cities, we
develop a satellite imagery dataset using deep learning models for five SDGs
containing 25 sustainable development indicators. The proposed dataset covers
the 100 most populated U.S. cities and corresponding Census Block Groups from
2014 to 2023. Specifically, we collect satellite imagery and identify objects
with state-of-the-art object detection and semantic segmentation models to
observe cities' bird's-eye view. We further gather population, nighttime light,
survey, and built environment data to depict SDGs regarding poverty, health,
education, inequality, and living environment. We anticipate the dataset to
help urban policymakers and researchers to advance SDGs-related studies,
especially applying satellite imagery to monitor long-term and multi-scale SDGs
in cities.
