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.

为支持美国城市可持续发展目标研究，我们利用深度学习模型开发了一个卫星图像数据集，涵盖了五个可持续发展目标和 25 个指标。该数据集覆盖了美国最多人口的 100 个城市以及对应的人口普查街区组，时间跨度为 2014 年至 2023 年。该数据集将帮助城市决策者和研究人员推进城市中与可持续发展目标相关的研究，尤其是应用卫星图像监测城市中的长期和多尺度可持续发展目标。